Metrological Analysis and Multi objective optimization of 3D Scanning Parameters for precise scanning of patient-specific dental models

Metrological Analysis and Multi objective optimization of 3D Scanning Parameters for precise scanning of patient-specific dental models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metrological Analysis and Multi objective optimization of 3D Scanning Parameters for precise scanning of patient-specific dental models Sumit Gahletia, Ramesh Kumar Garg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7145046/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract While the dental 3D scanner market is projected to surge towards a $ 2.61 billion valuation by 2032, with a robust 9.1% compound annual growth rate (CAGR), the fundamental accuracy underpinning its diagnostic promise is crucial for addressing the oral health of nearly 3.5 billion individuals, which remains intrinsically tied to the often-overlooked optimization of its scanning parameters. Building upon this confluence of rapidly increasing technology and pressing oral health crisis, proposed research aims to optimize the process parameters of a handheld 3D scanner for accurate and expedite scanning of patient-specific denture models. Scanning experiments are performed at the parametric combination of scanning parameters (scanning speed, angular orientation, and light intensity) is retrieved using the design of experiments methodology for desired output responses (standard deviation and scanning time). Furthermore the present research employ the potential of metaheuristic optimization algorithms, specifically an implementation of the NSGA-II (Non dominating sorted genetic algorithm) framework. The Artificial Neural Network model trained on an initial dataset of scan runs, to predict the accuracy and scan time across the parameter space, thereby significantly reducing the computational cost associated with exhaustive experimental trials. Subsequently, the Multi-Objective Genetic Algorithm will leverage this trained ANN to efficiently explore the Pareto front, identifying the non-dominated solutions that represent the optimal trade-offs between scanning accuracy, quantified through rigorous metrological analysis comparing the digital models to the standard deviation and scanning time. The primary emphasis of this research is to establish a scientifically validated, data-driven protocol for optimizing dental 3D scanning, thereby ensuring that this transformative technology realizes its full potential in delivering precise, efficient, and ultimately, improved patient care on a global scale. 3D Scanning patient specific retainer Dimensional Accuracy Optimization NSGA-II Digital Dentistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 1. Introduction The intricate landscape of dental care is undergoing a profound metamorphosis, driven by the relentless march of technological innovation. It is with this consideration that current studies embark on an exploration into the rapidly increasing realm of dental 3D scanners, a pivotal technology poised to redefine diagnostic and treatment paradigms. The preliminary analysis, grounded in market projections, reveals an astonishing trajectory, a compound annual growth rate of 9.1% is anticipated to propel this sector from its present state to a staggering $ 2.61 billion by the year 2032. This exponential expansion, however, is not merely a testament to technological prowess but it is inextricably linked to a far more fundamental and pressing global health concern. Considering the sobering statistic, approximately 3.5 billion individuals worldwide grapple with the pervasive burden of oral diseases. Hence, the ongoing research avenues should focus on helping the symbiotic relationship between these two seemingly disparate forces ie. Rapid proliferation of advanced digital imaging and the widespread prevalence of conditions demanding more precise and efficient interventions. Traditionally, medical experts assess the physique manually or use specific devices to aid in diagnosis and treatment decisions. In modern practice, computed tomography scanners (CT scanners), ultrasound, X-rays, and magnetic resonance imaging (MRI) are employed to produce comprehensive 3D impressions of the patient’s body. With the advancement in 3D scanning technology, a new clinical application field is emerging. Contact scanners such as Coordinate Measuring Machine systems (CMM) with touch-trigger probes (TTPs) offer high accuracy by developing point acquisition either continuously or point-to-point [ 1 ]. However, their primary drawback is the slow data acquisition process which becomes more challenging when measuring curved, free-form, or complex geometry[ 2 ]. In contrast, optical 3D scanners enable highly accurate and precise 3D measurements of the human body, including shape, size, color, texture, or skin-surface area, in a convenient and non-invasive manner without physical contact[ 3 , 4 ]. Optical 3D scanners capture millions of point data and generate a 3D image of complex objects within a short time. The scanner's specific arrangement in relation to the object and various input parameters influence the scanner’s capture capability, as illustrated in Fig. 1 [ 5 ]. Their scanning process relies on various principles including time of flight, laser triangulation, photogrammetry, and structured light[ 6 ]. Laser triangulation systems (LTSs) are preferred over other methods due to their lower cost, faster operation, and adequate integration[ 1 ]. In LTS, a laser is projected onto an object and its image is captured by a charge-coupled device (CCD) which serves as an image-capturing device, as shown in Fig. 2 . The 3D coordinates of the object points are then obtained using the triangulation principle and image processing techniques[ 7 – 9 ]. To achieve complete surface digitization, relative movement between the object and the sensor is required. Therefore, LT probes are mounted accordingly to maintain displacement relative to their axes, similar to CMM systems. For optimal orientation of the laser beam and object surface, thereby improving digitizing performance, an additional axis of angular movement can be incorporated. The sweeping (relative displacement between the object and the laser beam) projects the laser displacement over the object’s surface, generating a set of digitized point clouds. Further, precise calculations of each point’s position in space are performed in the CCD to create a 3D impression of the object [ 10 ]. The scanning results depend on several factors, including the quality and geometry of the object’s surface, environmental conditions, and the characteristics of the LTS [ 11 ]. These factors influence the intensity and shape of the laser gleam on the surface as well as the image captured by the sensor. Optical 3D imaging technology is extensively utilized in orthodontics, aiding in treatment planning and the precise customization of therapeutic devices like sequential aligners and fixed appliances [ 12 ]. Intraoral and extra-oral 3D scanners are employed to obtain highly accurate and precise digital impressions. Specifically, orthodontic aligner fitting benefits from the efficiency, comfort, and precision of 3D scanning and printing [ 13 – 16 ]. The patient’s dental arches are scanned using 3D scanners to create a digital impression, which is then used for printing aligners. These printed orthodontic aligners help maintain the corrected position of previously misaligned teeth, preventing them from reverting to their original alignment by fitting after teeth[ 17 , 18 ]. However, obtaining accurate and precise digital scans remains challenging for dental specialists due to several obstacles including uneven or complex dentition, moist oral environment, requirement of matting powder, patient or camera motion during scanning, and a restricted digitized surface area [ 19 , 20 ]. Determining the optical parameters of a 3D scanner for generating dental models is also an intricate task. This intricacy arises from the complex geometry of dental arches, the wide range of available scanner models, and the environmental variations during scanning, making the process particularly challenging [ 21 ]. Determining the optimal process variables in various clinical environments and determining their impact on improving diagnostic and treatment outcomes while minimizing the number of steps required for a complete scan remains a key challenge in laser scanning. Some key input parameters, including light intensity, scanning angle, and scanning distance, significantly affect the scan results. These parameters must be carefully chosen to ensure high accuracy. Several other parameters also play crucial roles in the scanning process and have a significant impact on scan quality. Despite numerous research findings in this field, several research gaps still exist, as summarized in Fig. 3 [ 21 ]. The present research lays down a foundation stone for the researches that delve into the quantifiable impact of dental 3D scanners on addressing this critical global health challenge, seeking to unravel the mechanisms by which this technological surge can translate into tangible improvements in patient outcomes and a reduction in the staggering burden of oral disease affecting nearly half of humanity. The proposed research, therefore, envisions a meticulously controlled experimental setup wherein patient-specific denture models, representing the diverse and complex geometries encountered in clinical practice, will serve as our primary subjects. The fundamental essence of scientific rigor demands a systematic exploration of the parameter space and hence, the present research methodically varies the significant scanning parameters (including scanning distance, scanning angle, and light intensity), meticulously documenting the resultant digital output responses. Furthermore, to navigate this multi-dimensional parameter landscape and identify the optimal configurations that yield the highest accuracy, the proposed research employ the power of metaheuristic optimization algorithms, specifically implementation of the Non dominated sorting genetic algorithm NSGA–II coupled with Artificial Neural Networks) framework. The Artificial Neural Network will serve as a surrogate model, trained on an initial dataset of scan runs, to predict the accuracy and scan time across the parameter space, thereby significantly reducing the computational cost associated with exhaustive experimental trials. Subsequently, the Multi-Objective Genetic Algorithm will leverage this trained ANN to efficiently explore the Pareto front, identifying the non-dominated solutions that represent the optimal trade-offs between scanning accuracy, quantified through rigorous metrological analysis comparing the digital models to the physical reference standards, and scanning time. 2. Experimental Layout and Implementation The proposed research follows a structured pre-experimental strategy aimed at optimizing the 3D scanning process parameters with a primary goal to assess the dimensional accuracy of maxillary (upper jaw) and mandibular (lower jaw) dental models under varying scanning conditions. The Calibry Mini 3D Scanner is utilized for capturing high-precision digital representations of dental models. This scanner is selected due to its portability, accuracy, and ability to generate dense point clouds for detailed surface mapping. It employs laser triangulation scanning technology, which enhances scan resolution and minimizes distortion. The scanner's capability to capture intricate dental structures makes it an ideal choice for orthodontic applications. 2.1.1 Selection of Scanning Parameters The accuracy and reliability of 3D scanning are influenced by several factors. In this study, three critical scanning parameters are investigated: Scanning Angle: The scanner is positioned at angles ranging from 30° to 105° to evaluate how angular orientation affects dimensional accuracy. Light Intensity: The impact of ambient lighting conditions is examined by varying light intensity between 10 W/m² and 37 W/m² to understand its role in scan clarity. Scanning Distance: The scanner is placed at different distances, from 20 cm to 60 cm, to determine the optimal range for accurate data acquisition. To systematically analyze the effects of these parameters, a Design of Experiments (DOE) approach is employed. The experimental conditions are structured using a factorial design table, ensuring a balanced investigation of all parameter interactions. The total number of experimental trials is calculated using Equation 1: (1) where: N = Total number of experiments K = Number of factors C = Number of center points Based on this methodology, 20 experimental runs are conducted, including factorial, axial, and central trials. These runs enhance the robustness of the optimization process and ensure a comprehensive evaluation of scanning parameter influences. To model parameter interactions and visualize the response surface for optimization, Response Surface Methodology (RSM) is applied. Specifically, a Face-Centered Composite Design (FCCD) is implemented due to its effectiveness in capturing nonlinear relationships between factors and responses. This approach helps in identifying the best scanning conditions for achieving high-precision 3D models. 2.1.2 Dental Model Preparation To evaluate scanning accuracy, two different dental models are selected: Maxillary (Upper Jaw) Model: Represents the teeth located in the upper jaw. Mandibular (Lower Jaw) Model: Represents the teeth located in the lower jaw. Both models are fabricated using dental-grade resin material, ensuring stability and consistent surface properties. Before scanning, the models are carefully cleaned and dried to eliminate any potential interference caused by dust, moisture, or external reflections. These precautions ensure that the scanning results are not affected by external contaminants, leading to more reliable data acquisition. 2.2 Experimental Analysis The scanning process is conducted using the Calibry Mini 3D Scanner, following the predefined experimental design. The key steps involved in the experimental analysis include: 2.2.1 3D Scanning Procedure The upper jaw and lower jaw models are placed on a stable surface to minimize vibrations during scanning. The Calibry Mini 3D Scanner is positioned at different angles and distances as per the DOE table. Multiple scans are performed under varying light intensity conditions to analyze the effect of illumination on scan accuracy. The point cloud data is collected for each scan, capturing intricate details of the dental models. 2.2.2 Data Processing and Accuracy Assessment The scanned point cloud data is processed using Geomagic Control X software , a widely used tool for metrology-based dimensional analysis . The following steps are performed: Point Cloud Processing: The raw scan data is converted into a high-resolution 3D mesh model . Alignment with Reference CAD Model: The scanned model is compared against an ideal CAD model to detect dimensional deviations. Deviation Analysis: A color-coded deviation map is generated, highlighting areas of inaccuracy in the scanned model. Error Quantification: The dimensional accuracy is evaluated using statistical error metrics, including: Root Mean Square Error (RMSE) Mean Deviation (MD) Standard Deviation (SD) These analyses help in identifying the most optimal scanning conditions for achieving high-precision dental models. 2.3 Post-Experimental Analysis Following the scanning process, a post-experimental analysis is conducted to optimize and validate the scanning parameters, ensuring the highest possible accuracy in the generated dental models. This phase integrates multi-objective optimization techniques to refine scanning conditions based on the observed deviations in the scanned models. 2.3.1 Multi-Objective Optimization Using NSGA-II To enhance the accuracy and efficiency of the scanning process, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is implemented. This advanced evolutionary algorithm is particularly effective in solving complex multi-objective problems by optimizing multiple conflicting parameters simultaneously. The optimization workflow consists of the following steps: Input of Experimental Data: The deviation values obtained from the Geomagic Control X software are utilized as input data for the optimization process. Objective Function Development: A mathematical model is formulated to minimize both standard deviation and scanning time while maintaining high precision. Non-Dominated Sorting and Crowding Distance: The algorithm ranks candidate solutions into Pareto-optimal fronts, prioritizing solutions that minimize deviation while ensuring diversity in the solution space. Selection of Optimal Scanning Conditions: The final optimized parameter set is selected based on Pareto-optimal solutions, balancing dimensional accuracy and scanning efficiency. 2.3.2 Validation of Optimized Parameters To assess the effectiveness of the optimized scanning parameters, a validation study is conducted by rescanning new dental models under the refined conditions. The accuracy of these newly generated models is then evaluated using deviation analysis. If the optimized conditions consistently produce lower deviations compared to initial scans, they are deemed effective for practical implementation in orthodontic applications. This structured methodology provides an inclusive framework for assessing the impact of 3D scanning parameters on the accuracy of orthodontic retainers, ensuring precise and reliable results. Figure 4, illustrates the structured workflow of the study, including pre-experimental strategy, experimental analysis, and post-experimental analysis . It highlights the key parameters considering the scanning and analysis process, and the optimization framework using a Genetic Algorithm (GA) to enhance dimensional accuracy. All these are summarized in the Table 1. Table 1: Summary of Methodology for Evaluating 3D Scanning Parameters Stage Key Activities Tools Used Pre-Experiment Strategy Selection of 3D scanner, dental model preparation, DOE-based scanning parameter selection Calibry Mini 3D Scanner, DOE Table Experimental Analysis 3D scanning under varied conditions, point cloud data collection, deviation analysis Geomagic Control X, Statistical Analysis Post-Experimental Analysis Genetic Algorithm-based optimization, validation through repeated trials Genetic Algorithm Model, MATLAB 3. Experimental Details 3.1 Technical Specifications of the 3D Scanner The 3D scanner utilized in this study is designed for high-precision surface digitization, ensuring accurate data acquisition for detailed geometric modeling. The device offers a measurement accuracy of up to 0.07 mm, making it suitable for applications requiring fine surface detail. Additionally, its distance-based accuracy can achieve up to 0.1 mm over a 1-meter scanning range, ensuring minimal distortion in large-scale captures. The point precision, reaching 0.15 mm, allows for highly detailed surface reconstructions, which is critical in dental and industrial applications. The scanner enables adaptability for objects of varying sizes and shapes. Its field of view (FOV) ranges from 86 × 115 mm (minimum) to 144 × 192 mm (maximum), allowing flexible scanning coverage based on the complexity of the object. The device incorporates texture mapping capabilities, enhancing the scanned models with realistic surface details. The illumination source consists of a Blue LED light, which helps in reducing interference from ambient lighting and improving scan clarity. The scanner operates at a frame capture rate of 25 to 30 frames per second, ensuring smooth data collection without lag. Additionally, with a data capture speed of 3 million points per second, the scanner efficiently acquires dense point cloud data for high-resolution 3D models. To enhance processing efficiency, multi-core processing is supported, enabling faster data handling and real-time rendering. The device is lightweight, weighing 900 grams (1.9 pounds), making it portable and user-friendly for extended scanning sessions. The scanner is equipped with a 4-inch touchscreen display, allowing for real-time monitoring and parameter adjustments. It comes bundled with dedicated software, facilitating seamless integration with post-processing workflows. The operating temperature between +5°C and +40°C certifies reliable performance across diverse environmental conditions. These technical specifications collectively contribute to the scanner’s capability to deliver high-fidelity 3D models, making it a valuable tool for precision-dependent applications such as dentistry, engineering, and medical imaging. Technical Specifications of the 3D Scanner defined in the table 2. Table 2: Technical Specifications of the 3D Scanner Parameter Specification Measurement Accuracy ±0.07 mm Distance-Based Accuracy ±0.1 mm per meter Point Precision ±0.15 mm Scanning Depth Range 180 mm - 300 mm Minimum Field of View 86 × 115 mm Maximum Field of View 144 × 192 mm Texture Mapping Enabled Illumination Source Blue LED Light Frame Capture Rate 25 - 30 frames per second Data Capture Speed 3 million points per second Multi-Core Processing Supported Device Weight 900 grams (1.9 pounds) Touchscreen Display Yes, 4-inch screen Bundled Software Included with the scanner Operating Temperature Range +5°C to +40°C 3.2 Calibration of Calibry Mini Nest 3D Scanner To ensure accurate data acquisition, the Calibry Mini 3D scanner is calibrated before scanning. The calibration procedure is crucial for minimizing errors in point cloud generation and improving the precision of surface reconstruction. The following steps outline the calibration process: Preparation of the Calibration Board A factory-provided calibration panel with a predefined pattern is securely placed on a stable surface to maintain a fixed reference. Proper lighting conditions are ensured to avoid reflections or uneven illumination, which may affect calibration accuracy. Launching Calibration Mode in Calibry Nest The Calibry Nest software is opened, and the scanner is connected to the system. The calibration mode is selected, initiating the process where the scanner recognizes the calibration pattern. Positioning the Scanner at Optimal Distance The scanner is held at the recommended distance, ensuring that the entire calibration board is within the field of view. The software provides real-time feedback, assisting in achieving the correct positioning. Capturing Multiple Angles The scanner is gradually tilted and rotated to different orientations, allowing it to capture the reference pattern from various perspectives. This step ensures calibration across all possible scanning angles. Processing and Adjustment Once the required images are captured, the software processes the calibration data and automatically adjusts internal parameters, including lens distortion correction, depth accuracy, and alignment precision. Verification and Finalization The system verifies calibration accuracy by comparing the scanned reference with the stored pattern. If deviations exceed the acceptable threshold, recalibration is performed. Upon successful verification, the calibration process is finalized, and the scanner is ready for precise 3D data acquisition. Figure 5 illustrates the structured workflow of the of calibration of 3D Scanner This calibration procedure is performed periodically or whenever scanning accuracy discrepancies are observed to ensure consistent and reliable results in 3D model generation. 3.3 Scanning Experimentations and DoE Runs Figure 6 provides a comprehensive representation of the 3D scanning workflow , outlining key stages involved in the digitization and evaluation process. The workflow begins with the experimental setup for 3D scanning , where a structured environment is arranged to ensure precise data acquisition. A Calibry Mini 3D scanner is utilized to capture detailed surface geometries of the dental models under controlled conditions. The next step involves the interface and functional workflow of Calibry Mini Nest software , where the scanned data is processed, refined, and converted into a digital format suitable for further analysis. In the scanning procedure, the typodont model is carefully positioned over a swivel table facilitating controlled rotational motion while maintaining a fixed scanner position. This setup ensures uniform data acquisition from multiple angles without altering the scanner's placement, thereby improving the precision of the captured model. To regulate the influence of ambient lighting conditions, a light intensity meter is employed to measure and monitor the surrounding illumination, preventing potential distortions in the scanned data. Additionally, the angular orientation of the scanner is determined using a gyroscope-based sensor embedded in a smartphone, which allows for precise adjustments in scanning angles. This systematic approach helps in optimizing the scanning parameters, ensuring high dimensional accuracy and consistency in the digital reconstruction of the dental model. Once the initial scanning is completed, the optimized 3D scan model is generated after parameter refinement , ensuring that the acquired data aligns with the required accuracy standards. Various scanning parameters, such as scanning angle, distance, and illumination settings, are optimized to enhance the fidelity of the final model. The last stage involves a 3D comparison of the scanned models in Geomagic Control X TM software , where the accuracy of the scanned model is evaluated by comparing it with a reference dataset. This comparison aids in identifying deviations and optimizing scanning techniques for improved precision. The interconnected steps in Figure 6 illustrate a structured methodology for high-accuracy 3D scanning , emphasizing the importance of parameter control, software processing, and accuracy validation in achieving reliable digital reconstructions. The various scanning parameters and their respective ranges considered in this study are detailed in Table 3. To systematically determine the optimal parameter combinations, the Design of Experiments (DoE) methodology is employed. This approach minimizes redundant trials by focusing on the most influential factors that affect the scanning outcomes. By efficiently analyzing different parameter settings, DoE helps in identifying the critical conditions required to enhance scanning accuracy and model precision. This ensures that the experiments are conducted in a structured manner, leading to improved data reliability and a more efficient scanning process. Table 3: Different Scanning Parameters and Their Range for both lower and upper denture model Factor Name Units Type Sub Type Minimum Maximum A Scanning Distance cm Numeric Continuous 20.00 60.00 B Scanning Angle Degree Numeric Continuous 35.00 105.00 C Light Intensity Watt/m² Numeric Continuous 10.00 37.00 Figure 7 illustrates the generated lower dental CAD models using parametric combinations derived through the Design of Experiments (DoE) as per table 4 methodology. A face-centered central composite design was employed to systematically analyze the impact of different scanning parameters on the final 3D reconstruction. Twenty experimental trials were conducted, where each iteration involved scanning a physical lower denture model to obtain its corresponding digital representation. The study controlled key input variables to improve model accuracy and ensure that the scanned data preserved the intricate anatomical details of the denture. The effect of the scanning angle was examined by positioning the scanner at different orientations, ranging from 30° to 105°, to assess its influence on dimensional accuracy. Additionally, light intensity variations between 10 W/m² and 37 W/m² were tested to determine the role of illumination in data acquisition. The impact of scanning distance was also evaluated by adjusting the scanner's position between 20 cm and 60 cm to identify the optimal range for capturing precise surface details. This systematic approach enabled the refinement of scanning parameters, leading to enhanced accuracy and reliability in the generation of lower dental CAD models. Table 4: Design of Experiment of lower dental model with corresponding outputs Sr. No. Scanning Distance Scanning Angle Light Intensity Standard Deviation Scanning Time 1 35 67.5 24 0.4833 92 2 35 67.5 24 0.4677 87 3 35 67.5 24 0.4838 88 4 35 67.5 24 0.477 92 5 20 45 32 0.5168 89 6 20 90 16 0.6353 88 7 35 67.5 37 0.6857 87 8 9 67.5 24 0.5227 86 9 35 67.5 10 0.4656 89 10 20 90 32 0.634 90 11 35 30 24 0.5097 91 12 50 90 16 0.4859 89 13 35 105 24 0.6692 88 14 50 45 32 0.4336 89 15 50 45 16 0.5406 87 16 35 67.5 24 0.5137 90 17 35 67.5 24 0.4648 86 18 20 45 16 0.5523 87 19 60 67 24 0.6699 89 20 50 90 32 0.4482 88 Figure 8 illustrates the generated upper dental CAD models by utilizing parametric combinations obtained through the Design of Experiments as per table 5 (DoE) methodology. A face-centered central composite design was implemented to conduct twenty experimental trials, each incorporating different variations in input parameters to assess their influence on scanning precision. During each scan, a physical upper denture model was digitized, and the acquired data was processed to develop an accurate 3D CAD representation. This systematic approach ensures the detailed capture of dental morphology while refining scanning conditions for improved dimensional accuracy and consistency in digital modeling. The study evaluates key scanning parameters, including scanning angle, which varies between 30° and 105° to analyse its effect on dimensional precision. Additionally, light intensity is adjusted within a range of 10 W/m² to 37 W/m² to investigate its impact on scan quality. Furthermore, the scanning distance is modified between 20 cm and 60 cm to determine the optimal range for precise data acquisition. Table 5: Design of Experiment of Upper dental model with corresponding outputs Sr. No. Scanning Distance Scanning Angle Light Intensity Standard Deviation Scanning Time 1 35 67.5 24 1.4733 92 2 35 67.5 24 1.4509 87 3 35 67.5 24 1.5055 88 4 35 67.5 24 1.6902 92 5 20 45 32 1.6809 89 6 20 90 16 1.5914 88 7 35 67.5 37 1.6331 87 8 9 67.5 24 1.551 86 9 35 67.5 10 1.6974 89 10 20 90 32 1.6902 90 11 35 30 24 1.5953 91 12 50 90 16 1.5634 89 13 35 105 24 1.5932 88 14 50 45 32 1.6094 89 15 50 45 16 1.5223 87 16 35 67.5 24 1.649 90 17 35 67.5 24 1.7315 86 18 20 45 16 1.5093 87 19 60 67 24 1.6096 89 20 50 90 32 1.4103 88 Standard deviation and scanning time are utilized as key quality indicators to evaluate the accuracy and reliability of different parameter sets used in the scanning process. These metrics serve as output responses to determine the influence of scanning parameters, due to their significance in quality control, sensitivity analysis, and statistical validation. The variation in standard deviation for upper and lower dental models across various scanning conditions is depicted. The generated point cloud data includes a standard deviation, indicating the degree of variation from the mean. The accuracy of the scanned model is evaluated by comparing the captured surface data with the reference STL file, which is created based on prescribed scanning protocols and verified through preliminary trials. Standard deviation serves as a measure of scan consistency and precision relative to the mean. To enhance processing efficiency, the data point density should be optimized, ensuring a balance between computational speed and sufficient feature representation. If the scan obtained using the specified parameters demonstrates improved precision and higher quality compared to a scan conducted without specific parameter adjustments, it indicates the effectiveness of the chosen parameters in enhancing scan quality. Conversely, if minimal or no improvement is observed, it may suggest that the selected parameters are unnecessary or that alternative parameters require adjustment to refine the scan output. The efficacy of scanning variables can be assessed by systematically comparing scans with and without parameter adjustments, allowing for further optimization of the scanning process to achieve superior accuracy and reliability. Figures 9a and 9c present the reference data of the lower and upper dental scan model primitive surfaces of the model geometry using different colors within the inspection software, while Figures 9b and 9d illustrate the measured data of the lower and upper dental scan model in various orientations. Additionally, Figures 10a and 10d visualize the initial alignment of lower and upper dental model respectively. Figures 10b and 10e visualize the optimized Best-Fit lower and upper dental scan model respectively. Figures 10c and 10f provide a 3D comparison of the scan models, demonstrating the initial alignment of scan models with reference data. 3.4 Model Development Given the intricate nature of parametric interactions in 3D scanning optimization, traditional empirical methods often struggle to accurately acquire the complex relationships between input parameters and output performance metrics. To overcome this challenge, a machine learning-based modeling approach was employed in this study. Specifically, a Back Propagation Artificial Neural Network (BPANN) was utilized to develop a predictive model capable of mapping nonlinear dependencies between scanning parameters and performance outcomes with high precision. 3.4.1 Model Selection Criteria The selection of an optimal model was based on two key performance indicators: Coefficient of Determination (R²): This statistical measure determines the extent to which the independent variables account for the variation in the dependent variable. A greater R² value indicates a stronger correlation between predicted and actual values, signifying the model's ability to accurately represent the data patterns. Root Mean Square Error (RMSE): RMSE measures the average deviation between predicted and actual values, reflecting the overall accuracy of the model. A lower RMSE signifies better predictive performance, ensuring minimal discrepancies between estimated and observed data points [22]. By analyzing these performance metrics, the most suitable BPANN model configuration was identified, ensuring a reliable and robust representation of the correlation between input scanning parameters and the resulting scan quality. This model served as the foundation for further optimization, enabling systematic exploration of parameter settings to achieve optimal scanning performance. 3.4.2 BPANN Model Architecture and Training The BPANN model employed in this study consisted of three primary layers: Input Layer: Comprising neurons corresponding to the selected 3D scanning parameters, including scanning distance, scanning angle, and light intensity. These parameters were chosen due to their significant influence on scan accuracy and processing time. Hidden Layer(s): A set of neurons designed to capture complex nonlinear interactions between input parameters. The number of hidden neurons was optimized through iterative experimentation to accomplish the best trade-off between model accuracy and computational efficiency [23]. Output Layer: Consisting of neurons representing the performance metrics, including standard deviation and scanning time, which were the key optimization objectives in this study. To enhance learning efficiency and avoid overfitting, the model was trained using the Levenberg-Marquardt algorithm, a widely used optimization technique in neural networks. The dataset was bifurcate into training (70%), validation (15%), and testing (15%) subsets to ensure unbiased performance evaluation. 3.4.3 Model Performance Evaluation The BPANN model was iteratively trained and tested using a dataset generated from experimental trials. Performance was assessed by comparing predicted values against actual experimental results, evaluating R² and RMSE values to determine accuracy. The final trained model exhibited a high R² value (>0.95) and a low RMSE, confirming its effectiveness in predicting scanning outcomes. The developed BPANN model provides a data-driven framework for analyzing the influence of 3D scanning parameters, enabling researchers and practitioners to fine-tune process settings for optimal scan quality and efficiency. By leveraging machine learning techniques, this approach eliminates the limitations of conventional trial-and-error methods, facilitating precise and reliable parameter optimization for advanced scanning applications [24,25]. 3.5 Multi-Objective Optimization Algorithms (NSGA-II) The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is a well-established multi-objective optimization technique, particularly efficient in solving complex engineering problems by balancing competing objectives. As an extension of the traditional genetic algorithm (GA), NSGA-II incorporates key enhancements, including elitism and crowding distance, to ensure robust convergence and solution diversity [26,27]. 3.5.1 NSGA-II Implementation in 3D Scanning Optimization In this study, NSGA-II was utilized to optimize 3D scanning parameters, focusing on scanning distance, scanning angle, and light intensity. These parameters significantly influence the accuracy, standard deviation, and scanning time in 3D scanning processes [28]. The optimization aimed to minimize both standard deviation and scanning time while maintaining high precision in the generated 3D models [27]. The algorithm commences with generating an initial population of candidate solutions, where each individual represents a unique combination of scanning parameters within predefined constraints. The constraints applied in this study were: 20 ≤ X1 ≤ 60 30 ≤ X2 ≤ 105 10 ≤ X3 ≤ 37 Each candidate solution was evaluated based on its fitness values, which were determined using a dominance-based ranking system [28]. Solutions that outperformed others in at least one objective without being worse in any other were assigned to the first Pareto front [29]. The remaining solutions were assigned to subsequent fronts on the basis of their dominance relationships [26]. To maintain diversity, NSGA-II employs a crowding distance metric, which measures the density of solutions around an individual in the objective space [28]. This ensures that selected solutions are well-distributed across the Pareto front, preventing premature convergence to a local optimum. The crowding distance approach favors solutions in sparsely populated regions, maintaining a diverse set of optimal solutions [27]. 3.5.2 Selection, Crossover, and Mutation Parents for the next generation were selected based on their Pareto rank and crowding distance [27]. Solutions with lower rank and higher crowding distance were given preference to maintain diversity and solution quality. The selected parents underwent: Recombination (Crossover): This step involved exchanging genetic material between parent solutions to create offspring that inherit advantageous traits. Mutation: Minor random modifications were applied to the offspring to explore unexplored areas of the solution space and avoid early convergence. To ensure population diversity, a reduction rate (r) of 0.1 was used, limiting the maximum number of solutions from each Pareto front to 10% of the total population size [28]. The new offspring population was then combined with the parent population, and a new generation was formed using a survivor selection strategy based on non-dominated sorting and crowding distance [30]. 3.5.3 Convergence and Stopping Criteria NSGA-II continued the optimization process iteratively until a predefined stopping criterion was met [28]. This included: Maximum Number of Generations: The algorithm was allowed to run for a set number of generations to explore the solution space sufficiently. Convergence of Pareto Front: If the Pareto front solutions stabilized and no significant improvements were observed in successive generations, the optimization process was terminated. The final Pareto-optimal solutions represent a set of trade-offs between scanning accuracy and scanning time, providing a comprehensive view of the best possible parameter configurations for 3D scanning [29]. These solutions offer decision-makers insights into the optimal balance between minimizing standard deviation and reducing scanning time, ensuring high-quality 3D models with efficient scanning performance [30]. 4. Results and Discussion The analysis of the artificial neural network (ANN) provides valuable insights into the performance and predictive capabilities of the proposed model for optimizing 3D scanning parameters. The two-output neural network structure, as illustrated in Fig. 11 , demonstrates high accuracy in predicting scanning outcomes based on key input variables, such as scanning distance, scanning angle, and light intensity. Figures 12 a and 12 b illustrate that the model underwent periodic evaluations to maintain reliability and avoid excessive training. For the lower dental model, the ANN model attained a training R-value of 0.99965, demonstrating a strong correlation with the training dataset, while the testing R-value of 0.99955 indicates effective generalization to new data. Additionally, the validation R-value of 0.99971 highlights the model’s stability when handling new datasets. The overall R-value of 0.99961, as depicted in Fig. 13 a, confirms the strong predictive performance across all datasets. Similarly, for the upper dental model, the ANN yielded a training R-value of 0.99986, a testing R-value of 0.99919, and a validation R-value of 0.99964, resulting in an overall R-value of 0.99968 as depicted in Fig. 13 b. These consistently high R-values demonstrate the effectiveness of the model in capturing complex relationships between 3D scanning parameters and output metrics. Furthermore, the model’s training efficiency is evident in the mean squared error (MSE) values, with the lowest MSE of 1.1414 achieved at epoch 3 for the lower dental model (Fig. 14 a) and 2.5992 at epoch 3 for the upper dental model (Fig. 14 b). This indicates that the network reached optimal performance early in the training process, minimizing errors between predicted and actual values. The error histogram, as presented in Figs. 15 a and 15 b for the lower and upper dental models respectively, provides additional validation of the model’s accuracy. The histogram divides errors into 20 discrete bins, revealing that the training error is concentrated around an error value of 0.03856 for the lower dental model and 0.07397 for the upper dental model. The zero-error line in the histogram further reinforces the model’s precision, with most errors clustered close to zero, suggesting minimal deviation from actual values. Additionally, the gradient analysis highlights key convergence characteristics of the model. A small gradient value of 0.0087092 at epoch 9 for the lower dental model (Fig. 16 a) and 0.0024936 at epoch 9 for the upper dental model (Fig. 16 b) suggest that the model approached a point of diminishing returns in learning, ensuring stable optimization. The low Mu value of 1×10⁻⁷ at epoch 9 for both models indicates that the network made cautious weight updates, preventing overfitting while fine-tuning parameter relationships. The validation checks of 6 at epoch 9, as shown in Figs. 12 a and 12 b, confirms that the model was periodically assessed to ensure reliability and prevent unnecessary overtraining. The training process concluded once the validation criteria were met, achieving optimal predictive performance. The proposed NSGA II-ANN optimization framework was employed to investigate the influence of scanning distance, scanning angle, and light intensity on standard deviation and scanning time in 3D scanning models. Through experimental trials, critical process parameters were identified, confirming that integrating machine learning algorithms with 3D scanning optimization enables substantial reductions in scanning time while improving dimensional accuracy. The Pareto front solutions, presented in Figs. 17 a and 17 b, illustrate the trade-offs between competing objectives, guiding the selection of optimal scanning conditions. Tables 6 and 8 shows Pareto front values with corresponding outcomes of lower and upper dental models respectively. Table 6 Pareto front values with corresponding outcomes of lower dental model S. No Scanning Distance Scanning Angle Light Intensity Standard Deviation Scanning Time 1 20.9056 88.2818 29.6236 0.543 86.8526 2 21.3902 87.0197 31.1778 0.5127 86.9299 3 20.7985 88.28 30.6955 0.5263 86.8893 4 20.7144 88.3543 27.0181 0.584 86.7828 5 20.9036 88.135 29.3711 0.5468 86.8449 6 25.2964 59.5255 31.763 0.4643 87.3982 7 20.9638 88.0573 30.3197 0.5307 86.8812 8 22.5506 50.5624 31.9294 0.457 87.6431 9 26.7743 84.9965 31.9069 0.4803 87.0464 10 23.5732 61.7783 31.5983 0.4685 87.3016 11 20.7435 88.3453 27.3307 0.5798 86.7869 12 20.9178 88.218 30.9494 0.5214 86.9017 13 20.8035 88.1278 30.0872 0.5357 86.8682 14 22.5605 86.3245 31.0792 0.5063 86.9568 15 21.0877 87.91 27.7119 0.5713 86.8039 16 24.1045 49.7088 31.939 0.4562 87.7332 17 21.6364 86.4093 31.2948 0.5085 86.9438 18 21.1076 86.8372 28.3367 0.5592 86.8279 19 21.0111 87.8361 29.749 0.5391 86.8634 20 23.8076 81.7782 31.8963 0.4857 87.0322 21 21.0982 87.2895 31.0452 0.5169 86.9169 22 23.7889 56.8705 31.9415 0.4617 87.443 23 24.877 87.2708 31.3789 0.4936 86.999 24 20.8929 88.2429 30.8127 0.5237 86.896 25 27.0621 85.0335 31.8633 0.48 87.0493 26 24.4024 66.9867 31.9291 0.4701 87.2099 27 23.6389 87.3792 31.2227 0.5008 86.9732 28 20.8072 88.3097 28.2248 0.5664 86.8063 29 20.9015 88.1722 29.033 0.5525 86.8332 30 23.0523 85.0321 31.4213 0.4976 86.9857 31 20.878 88.1299 29.0786 0.5518 86.8345 32 22.5371 47.0464 31.9449 0.4544 87.7999 33 24.271 85.5599 31.8349 0.4891 87.0095 34 29.223 85.0746 31.824 0.4757 87.0724 35 23.8 45.5053 31.9997 0.4535 87.9258 Table 7 Result validation of lower dental model Sr. No. Scanning Distance Scanning Angle Light Intensity Predicted Standard Deviation Experimental Standard Deviation % Error Predicted Scanning Time Experimental Scanning Time % Error 1 23.8 45.5053 31.9997 0.4535 0.4718 3.87 87.9258 89 1.2 Table 8 Pareto front values with corresponding outcomes of Upper dental model S. No Scanning Distance Scanning Angle Light Intensity Standard Deviation Scanning Time 1 20.1062 45.0564 31.9404 1.5133 88.2729 2 22.4403 55.8848 31.9853 1.5505 87.5229 3 20.5038 48.2605 31.6045 1.5265 87.8606 4 20.7823 48.8074 31.9419 1.528 87.7992 5 20.1062 45.3064 31.9404 1.5143 88.2353 6 22.325 55.6227 31.978 1.5496 87.5241 7 21.6219 55.184 31.9221 1.5465 87.536 8 20.5378 49.6857 31.9434 1.5299 87.752 9 21.4665 50.9402 31.867 1.536 87.6448 10 21.8616 55.6 31.9424 1.5481 87.5296 11 20.5762 47.9875 31.719 1.5255 87.8801 12 20.4674 51.509 31.8664 1.5347 87.6594 13 20.3085 46.8836 31.7861 1.521 88.0092 14 20.7729 49.6739 31.8955 1.5306 87.7406 15 20.2833 47.8777 31.9665 1.5237 87.9119 16 20.3294 46.0867 31.6001 1.519 88.0953 17 20.3943 46.4481 31.9172 1.5194 88.0548 18 22.3553 53.1301 31.9755 1.5439 87.5504 19 21.3566 48.7321 31.8781 1.5295 87.7748 20 21.1616 54.3102 31.8824 1.5432 87.5564 21 22.4235 52.4557 31.9815 1.5425 87.5639 22 20.7628 50.4821 31.8951 1.5328 87.6943 23 21.287 49.4385 31.915 1.5313 87.7294 24 21.682 52.571 31.7655 1.5409 87.5877 25 20.3078 47.1397 31.8265 1.5218 87.9823 26 20.8464 50.7083 31.9518 1.5335 87.6771 27 20.4738 50.5191 31.9399 1.532 87.7059 28 20.8548 52.3849 31.9485 1.5378 87.608 29 21.6733 51.9608 31.9292 1.5391 87.5967 30 20.6062 45.3064 31.9404 1.5157 88.1848 31 20.9344 45.9777 31.8731 1.5193 88.0658 32 20.6791 45.4483 31.8518 1.5168 88.156 33 20.1062 45.0564 31.9404 1.5133 88.2729 34 20.541 46.5187 31.8948 1.5201 88.0337 35 20.2823 48.8074 31.9419 1.5266 87.8312 Table 9 Result validation of upper dental model Sr. No. Scanning Distance Scanning Angle Light Intensity Predicted Standard Deviation Experimental Standard Deviation % Error Predicted Scanning Time Experimental Scanning Time % Error 1 20.2823 48.8074 31.9419 1.5266 1.5707 2.8 87.8312 89 1.31 The optimal scanning parameters identified for the lower dental model included a scanning distance of 23.8 cm, a scanning angle of 45.5053°, and a light intensity of 31.9997 W/m². For the upper dental model, the best results were obtained with a scanning distance of 20.2823 cm, a scanning angle of 48.8074°, and a light intensity of 31.9419 W/m². Validation experiments, as presented in Tables 7 and 9 , demonstrated a strong correlation between the predicted and actual values, with minimal deviations observed in standard deviation and scanning time. The results underscore the impact of scanning parameters on accuracy and efficiency, offering practical guidance for optimizing 3D scanning workflows in dental applications. By leveraging NSGA II-ANN optimization, manufacturers and researchers can enhance the precision and reliability of dental scanning processes, ensuring high-quality outputs that meet clinical and industrial standards. Furthermore, the integration of advanced scanning techniques with AI-driven parameter tuning paves the way for improved fabrication of dental devices, contributing to personalized healthcare solutions and next-generation medical imaging technologies. 5. Conclusions and Future Scope The proposed research proficiently bridges the gaps of advanced digital imaging, computational optimization, and the pressing realities of global oral health, has yielded compelling insights into the critical role of scanning parameter optimization in maximizing the efficacy of handheld dental 3D scanners. The study concluded that subtle adjustments in parameters such as scanning distance, scanning angle and light inytensity can yield statistically significant improvements in both the accuracy of the digital models and the efficiency of the acquisition process. This optimization directly translates to enhanced diagnostic fidelity, particularly crucial in the early detection of conditions like dental caries affecting a vast majority of the global pediatric population, and improved clinical workflows, essential as the burden of periodontal diseases continues its upward trajectory, reaching a larger section of the global population. The implications of this research extend beyond the specific handheld scanner investigated. The MOGA-ANN optimization framework we have developed offers a generalizable paradigm for the systematic evaluation and refinement of scanning protocols across various dental 3D imaging technologies. Declarations The authors confirm that this study has no financial or personal conflicts of interest. 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4","display":"","copyAsset":false,"role":"figure","size":165085,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the Experimental Methodology for 3D Scanning of Dental Models\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/1fef2dc35da91cc376d93e45.jpg"},{"id":87695996,"identity":"4c5f65ab-338a-455f-91d2-13b553823d01","added_by":"auto","created_at":"2025-07-28 06:07:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106261,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the Calibry Mini Nest 3D Scanner Calibration Steps\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/43934095d2081da1f6526f69.jpg"},{"id":87695816,"identity":"9ec1ff02-4d09-4db2-8183-1adfc42fbaaa","added_by":"auto","created_at":"2025-07-28 05:59:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":119241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive Workflow of 3D Scanning, Processing, and Accuracy Evaluation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/029db00d6b26a5e17d8e7084.jpg"},{"id":87695143,"identity":"4b177c37-2a25-4041-9307-8cdb35e82286","added_by":"auto","created_at":"2025-07-28 05:51:47","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":169736,"visible":true,"origin":"","legend":"\u003cp\u003eGenerated lower Dental CAD Models Using DoE-Based Parametric Combinations\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/d02bbda0a24d0b3f2b154310.jpg"},{"id":87695822,"identity":"88e73eeb-7f88-47f9-b624-9f051bc4e71d","added_by":"auto","created_at":"2025-07-28 05:59:47","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":159165,"visible":true,"origin":"","legend":"\u003cp\u003eGenerated Upper Dental CAD Models Using DoE-Based Parametric Combinations\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/8254d0a10aad4eaf9e000358.jpg"},{"id":87695154,"identity":"03d5b333-7671-45d1-a503-e0c31d928de2","added_by":"auto","created_at":"2025-07-28 05:51:47","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":107306,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Reference data of lower dental scan model (b) measured data of lower scan \u0026nbsp;model (c) Reference data of Upper dental scan model (d) measured data of Upper scan model\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/8195d0b59db6c22ecc30a73f.jpg"},{"id":87695152,"identity":"91380207-83de-4960-8fc7-1f41b69ce31d","added_by":"auto","created_at":"2025-07-28 05:51:47","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":132374,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Visualization of Initial Alignment lower dental scan model (b) Visualization of Optimized Best-Fit lower dental scan model (c) 3D Comparison of lower dental scan model (d) Visualization of Initial Alignment Upper dental scan model (e) Visualization of Optimized Best-Fit upper dental scan model (f) 3D Comparison of lower dental scan model\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/41a29325c310d22f3a7ec0b9.jpg"},{"id":87697024,"identity":"7ffd57f9-891d-4f94-9ff4-6287a2421912","added_by":"auto","created_at":"2025-07-28 06:23:47","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":18398,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Diagram\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/33be17a76841143e1d5a3461.jpg"},{"id":87695163,"identity":"0758cd07-3ff8-4f92-ba3a-7e441af2fd33","added_by":"auto","created_at":"2025-07-28 05:51:47","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":93205,"visible":true,"origin":"","legend":"\u003cp\u003e(a) ANN Progress of lower dental model (b) ANN Progress of upper dental model\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/fe47b763e5d8c3879f7164db.jpg"},{"id":87695831,"identity":"aba1c96b-f0aa-4dbe-8e44-44d517f8c78b","added_by":"auto","created_at":"2025-07-28 05:59:48","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":79968,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Relative performance of ANN lower dental model (b) Relative performance of ANN upper dental model\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/dae40a2e983f6c1d74733e65.jpg"},{"id":87695166,"identity":"3c7869c2-7c30-4ea3-87ca-4f891440ffe9","added_by":"auto","created_at":"2025-07-28 05:51:47","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":44979,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Performance plot for ANN lower dental model (b)Performance plot for ANN lower dental model\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/5e4f949813e529ec71069bfb.jpg"},{"id":87695159,"identity":"55af2e76-9127-440e-974b-e0c56ecc0c9c","added_by":"auto","created_at":"2025-07-28 05:51:47","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":51049,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Error histogram of lower dental model (b) Error histogram of upper dental model\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/7a1705f93554fc5e208bf99d.jpg"},{"id":87695160,"identity":"1313e983-9c66-48ae-8025-68b35380de2d","added_by":"auto","created_at":"2025-07-28 05:51:47","extension":"jpg","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":51289,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Validation checks of lower dental model (b) Validation checks of upper dental model\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/86735b11463e72b5efa21cc0.jpg"},{"id":87695824,"identity":"72eb0805-480d-4609-954a-30ffafe33063","added_by":"auto","created_at":"2025-07-28 05:59:47","extension":"jpg","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":37391,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Pareto front plot of lower model (b) Pareto front plot of upper model\u003c/p\u003e","description":"","filename":"17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/78c23d29d42363ce94e80fbd.jpg"},{"id":88505185,"identity":"e83b0fca-0fbc-494f-99b2-1b6b5abc6183","added_by":"auto","created_at":"2025-08-07 07:20:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3994666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7145046/v1/6aa26a2c-ec22-42f9-bf99-6070c01a277f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metrological Analysis and Multi objective optimization of 3D Scanning Parameters for precise scanning of patient-specific dental models","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe intricate landscape of dental care is undergoing a profound metamorphosis, driven by the relentless march of technological innovation. It is with this consideration that current studies embark on an exploration into the rapidly increasing realm of dental 3D scanners, a pivotal technology poised to redefine diagnostic and treatment paradigms. The preliminary analysis, grounded in market projections, reveals an astonishing trajectory, a compound annual growth rate of 9.1% is anticipated to propel this sector from its present state to a staggering \u003cspan\u003e$\u003c/span\u003e2.61\u0026nbsp;billion by the year 2032. This exponential expansion, however, is not merely a testament to technological prowess but it is inextricably linked to a far more fundamental and pressing global health concern. Considering the sobering statistic, approximately 3.5\u0026nbsp;billion individuals worldwide grapple with the pervasive burden of oral diseases. Hence, the ongoing research avenues should focus on helping the symbiotic relationship between these two seemingly disparate forces ie. Rapid proliferation of advanced digital imaging and the widespread prevalence of conditions demanding more precise and efficient interventions.\u003c/p\u003e\n\u003cp\u003eTraditionally, medical experts assess the physique manually or use specific devices to aid in diagnosis and treatment decisions. In modern practice, computed tomography scanners (CT scanners), ultrasound, X-rays, and magnetic resonance imaging (MRI) are employed to produce comprehensive 3D impressions of the patient\u0026rsquo;s body. With the advancement in 3D scanning technology, a new clinical application field is emerging. Contact scanners such as Coordinate Measuring Machine systems (CMM) with touch-trigger probes (TTPs) offer high accuracy by developing point acquisition either continuously or point-to-point [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, their primary drawback is the slow data acquisition process which becomes more challenging when measuring curved, free-form, or complex geometry[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. In contrast, optical 3D scanners enable highly accurate and precise 3D measurements of the human body, including shape, size, color, texture, or skin-surface area, in a convenient and non-invasive manner without physical contact[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Optical 3D scanners capture millions of point data and generate a 3D image of complex objects within a short time. The scanner\u0026apos;s specific arrangement in relation to the object and various input parameters influence the scanner\u0026rsquo;s capture capability, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTheir scanning process relies on various principles including time of flight, laser triangulation, photogrammetry, and structured light[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Laser triangulation systems (LTSs) are preferred over other methods due to their lower cost, faster operation, and adequate integration[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. In LTS, a laser is projected onto an object and its image is captured by a charge-coupled device (CCD) which serves as an image-capturing device, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The 3D coordinates of the object points are then obtained using the triangulation principle and image processing techniques[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. To achieve complete surface digitization, relative movement between the object and the sensor is required. Therefore, LT probes are mounted accordingly to maintain displacement relative to their axes, similar to CMM systems. For optimal orientation of the laser beam and object surface, thereby improving digitizing performance, an additional axis of angular movement can be incorporated. The sweeping (relative displacement between the object and the laser beam) projects the laser displacement over the object\u0026rsquo;s surface, generating a set of digitized point clouds. Further, precise calculations of each point\u0026rsquo;s position in space are performed in the CCD to create a 3D impression of the object [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. The scanning results depend on several factors, including the quality and geometry of the object\u0026rsquo;s surface, environmental conditions, and the characteristics of the LTS [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. These factors influence the intensity and shape of the laser gleam on the surface as well as the image captured by the sensor.\u003c/p\u003e\n\u003cp\u003eOptical 3D imaging technology is extensively utilized in orthodontics, aiding in treatment planning and the precise customization of therapeutic devices like sequential aligners and fixed appliances [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Intraoral and extra-oral 3D scanners are employed to obtain highly accurate and precise digital impressions. Specifically, orthodontic aligner fitting benefits from the efficiency, comfort, and precision of 3D scanning and printing [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. The patient\u0026rsquo;s dental arches are scanned using 3D scanners to create a digital impression, which is then used for printing aligners. These printed orthodontic aligners help maintain the corrected position of previously misaligned teeth, preventing them from reverting to their original alignment by fitting after teeth[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, obtaining accurate and precise digital scans remains challenging for dental specialists due to several obstacles including uneven or complex dentition, moist oral environment, requirement of matting powder, patient or camera motion during scanning, and a restricted digitized surface area [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Determining the optical parameters of a 3D scanner for generating dental models is also an intricate task. This intricacy arises from the complex geometry of dental arches, the wide range of available scanner models, and the environmental variations during scanning, making the process particularly challenging [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Determining the optimal process variables in various clinical environments and determining their impact on improving diagnostic and treatment outcomes while minimizing the number of steps required for a complete scan remains a key challenge in laser scanning. Some key input parameters, including light intensity, scanning angle, and scanning distance, significantly affect the scan results. These parameters must be carefully chosen to ensure high accuracy. Several other parameters also play crucial roles in the scanning process and have a significant impact on scan quality. Despite numerous research findings in this field, several research gaps still exist, as summarized in Fig.\u0026nbsp;3 [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The present research lays down a foundation stone for the researches that delve into the quantifiable impact of dental 3D scanners on addressing this critical global health challenge, seeking to unravel the mechanisms by which this technological surge can translate into tangible improvements in patient outcomes and a reduction in the staggering burden of oral disease affecting nearly half of humanity.\u003c/p\u003e\n\u003cp\u003eThe proposed research, therefore, envisions a meticulously controlled experimental setup wherein patient-specific denture models, representing the diverse and complex geometries encountered in clinical practice, will serve as our primary subjects. The fundamental essence of scientific rigor demands a systematic exploration of the parameter space and hence, the present research methodically varies the significant scanning parameters (including scanning distance, scanning angle, and light intensity), meticulously documenting the resultant digital output responses. Furthermore, to navigate this multi-dimensional parameter landscape and identify the optimal configurations that yield the highest accuracy, the proposed research employ the power of metaheuristic optimization algorithms, specifically implementation of the Non dominated sorting genetic algorithm NSGA\u0026ndash;II coupled with Artificial Neural Networks) framework. The Artificial Neural Network will serve as a surrogate model, trained on an initial dataset of scan runs, to predict the accuracy and scan time across the parameter space, thereby significantly reducing the computational cost associated with exhaustive experimental trials. Subsequently, the Multi-Objective Genetic Algorithm will leverage this trained ANN to efficiently explore the Pareto front, identifying the non-dominated solutions that represent the optimal trade-offs between scanning accuracy, quantified through rigorous metrological analysis comparing the digital models to the physical reference standards, and scanning time.\u003c/p\u003e"},{"header":"2. Experimental Layout and Implementation","content":"\u003cp\u003eThe proposed research follows a structured pre-experimental strategy aimed at optimizing the 3D scanning process parameters with a primary goal to assess the dimensional accuracy of maxillary (upper jaw) and mandibular (lower jaw) dental models under varying scanning conditions. The Calibry Mini 3D Scanner is utilized for capturing high-precision digital representations of dental models. This scanner is selected due to its portability, accuracy, and ability to generate dense point clouds for detailed surface mapping. It employs laser triangulation scanning technology, which enhances scan resolution and minimizes distortion. The scanner\u0026apos;s capability to capture intricate dental structures makes it an ideal choice for orthodontic applications.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.1.1 Selection of Scanning Parameters\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe accuracy and reliability of 3D scanning are influenced by several factors. In this study, three critical scanning parameters are investigated:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eScanning Angle: The scanner is positioned at angles ranging from 30\u0026deg; to 105\u0026deg; to evaluate how angular orientation affects dimensional accuracy.\u003c/li\u003e\n \u003cli\u003eLight Intensity: The impact of ambient lighting conditions is examined by varying light intensity between 10 W/m\u0026sup2; and 37 W/m\u0026sup2; to understand its role in scan clarity.\u003c/li\u003e\n \u003cli\u003eScanning Distance: The scanner is placed at different distances, from 20 cm to 60 cm, to determine the optimal range for accurate data acquisition.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo systematically analyze the effects of these parameters, a Design of Experiments (DOE) approach is employed. The experimental conditions are structured using a factorial design table, ensuring a balanced investigation of all parameter interactions.\u003c/p\u003e\n\u003cp\u003eThe total number of experimental trials is calculated using Equation 1:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"120\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (1)\u003c/p\u003e\n\u003cp\u003ewhere:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eN = Total number of experiments\u003c/li\u003e\n \u003cli\u003eK = Number of factors\u003c/li\u003e\n \u003cli\u003eC = Number of center points\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBased on this methodology, 20 experimental runs are conducted, including factorial, axial, and central trials. These runs enhance the robustness of the optimization process and ensure a comprehensive evaluation of scanning parameter influences.\u003c/p\u003e\n\u003cp\u003eTo model parameter interactions and visualize the response surface for optimization, Response Surface Methodology (RSM) is applied. Specifically, a Face-Centered Composite Design (FCCD) is implemented due to its effectiveness in capturing nonlinear relationships between factors and responses. This approach helps in identifying the best scanning conditions for achieving high-precision 3D models.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.1.2 Dental Model Preparation\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eTo evaluate scanning accuracy, two different dental models are selected:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eMaxillary (Upper Jaw) Model: Represents the teeth located in the upper jaw.\u003c/li\u003e\n \u003cli\u003eMandibular (Lower Jaw) Model: Represents the teeth located in the lower jaw.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth models are fabricated using dental-grade resin material, ensuring stability and consistent surface properties. Before scanning, the models are carefully cleaned and dried to eliminate any potential interference caused by dust, moisture, or external reflections. These precautions ensure that the scanning results are not affected by external contaminants, leading to more reliable data acquisition.\u003c/p\u003e\n\u003ch3\u003e2.2 Experimental Analysis\u003c/h3\u003e\n\u003cp\u003eThe scanning process is conducted using the Calibry Mini 3D Scanner, following the predefined experimental design. The key steps involved in the experimental analysis include:\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.2.1 3D Scanning Procedure\u003c/strong\u003e\u003c/h4\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eThe \u003cstrong\u003eupper jaw and lower jaw models\u003c/strong\u003e are placed on a stable surface to minimize vibrations during scanning.\u003c/li\u003e\n \u003cli\u003eThe \u003cstrong\u003eCalibry Mini 3D Scanner\u003c/strong\u003e is positioned at different angles and distances as per the DOE table.\u003c/li\u003e\n \u003cli\u003eMultiple scans are performed under varying \u003cstrong\u003elight intensity conditions\u003c/strong\u003e to analyze the effect of illumination on scan accuracy.\u003c/li\u003e\n \u003cli\u003eThe \u003cstrong\u003epoint cloud data\u003c/strong\u003e is collected for each scan, capturing intricate details of the dental models.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003cstrong\u003e2.2.2 Data Processing and Accuracy Assessment\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe scanned point cloud data is processed using \u003cstrong\u003eGeomagic Control X software\u003c/strong\u003e, a widely used tool for \u003cstrong\u003emetrology-based dimensional analysis\u003c/strong\u003e. The following steps are performed:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003ePoint Cloud Processing:\u003c/strong\u003e The raw scan data is converted into a high-resolution \u003cstrong\u003e3D mesh model\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAlignment with Reference CAD Model:\u003c/strong\u003e The scanned model is compared against an \u003cstrong\u003eideal CAD model\u003c/strong\u003e to detect dimensional deviations.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDeviation Analysis:\u003c/strong\u003e A \u003cstrong\u003ecolor-coded deviation map\u003c/strong\u003e is generated, highlighting areas of inaccuracy in the scanned model.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eError Quantification:\u003c/strong\u003eThe dimensional accuracy is evaluated using statistical error metrics, including:\u003cul type=\"circle\"\u003e\n \u003cli\u003e\u003cstrong\u003eRoot Mean Square Error (RMSE)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMean Deviation (MD)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStandard Deviation (SD)\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese analyses help in identifying the most \u003cstrong\u003eoptimal scanning conditions\u003c/strong\u003e for achieving high-precision dental models.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.3 Post-Experimental Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eFollowing the scanning process, a post-experimental analysis is conducted to optimize and validate the scanning parameters, ensuring the highest possible accuracy in the generated dental models. This phase integrates multi-objective optimization techniques to refine scanning conditions based on the observed deviations in the scanned models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1 Multi-Objective Optimization Using NSGA-II\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;To enhance the accuracy and efficiency of the scanning process, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is implemented. This advanced evolutionary algorithm is particularly effective in solving complex multi-objective problems by optimizing multiple conflicting parameters simultaneously. The optimization workflow consists of the following steps:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eInput of Experimental Data:\u003c/strong\u003e The deviation values obtained from the Geomagic Control X software are utilized as input data for the optimization process.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eObjective Function Development:\u003c/strong\u003e A mathematical model is formulated to minimize both standard deviation and scanning time while maintaining high precision.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNon-Dominated Sorting and Crowding Distance:\u003c/strong\u003e The algorithm ranks candidate solutions into Pareto-optimal fronts, prioritizing solutions that minimize deviation while ensuring diversity in the solution space.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSelection of Optimal Scanning Conditions:\u003c/strong\u003e The final optimized parameter set is selected based on Pareto-optimal solutions, balancing dimensional accuracy and scanning efficiency.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2 Validation of Optimized Parameters\u0026nbsp;\u003c/strong\u003e\u003cbr\u003eTo assess the effectiveness of the optimized scanning parameters, a validation study is conducted by rescanning new dental models under the refined conditions. The accuracy of these newly generated models is then evaluated using deviation analysis. If the optimized conditions consistently produce lower deviations compared to initial scans, they are deemed effective for practical implementation in orthodontic applications. This structured methodology provides an \u003cstrong\u003einclusive framework\u003c/strong\u003e for assessing the impact of 3D scanning parameters on the accuracy of orthodontic retainers, ensuring precise and reliable results.\u003c/p\u003e\n\u003cp\u003eFigure 4, illustrates the structured workflow of the study, including \u003cstrong\u003epre-experimental strategy, experimental analysis, and post-experimental analysis\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e It highlights the key parameters considering the scanning and analysis process, and the optimization framework using a \u003cstrong\u003eGenetic Algorithm (GA)\u003c/strong\u003e to enhance dimensional accuracy. All these are summarized in the Table 1.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eSummary of Methodology for Evaluating 3D Scanning Parameters\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKey Activities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTools Used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePre-Experiment Strategy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSelection of 3D scanner, dental model preparation, DOE-based scanning parameter selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCalibry Mini 3D Scanner, DOE Table\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eExperimental Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3D scanning under varied conditions, point cloud data collection, deviation analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeomagic Control X, Statistical Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Experimental Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGenetic Algorithm-based optimization, validation through repeated trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGenetic Algorithm Model, MATLAB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"3. Experimental Details","content":"\u003cp\u003e\u003cstrong\u003e3.1 Technical Specifications of the 3D Scanner\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3D scanner utilized in this study is designed for high-precision surface digitization, ensuring accurate data acquisition for detailed geometric modeling. The device offers a measurement accuracy of up to 0.07 mm, making it suitable for applications requiring fine surface detail. Additionally, its distance-based accuracy can achieve up to 0.1 mm over a 1-meter scanning range, ensuring minimal distortion in large-scale captures. The point precision, reaching 0.15 mm, allows for highly detailed surface reconstructions, which is critical in dental and industrial applications.\u003c/p\u003e\n\u003cp\u003eThe scanner enables adaptability for objects of varying sizes and shapes. Its\u0026nbsp;field of view (FOV) ranges from 86 \u0026times; 115 mm (minimum) to 144 \u0026times; 192 mm (maximum),\u0026nbsp;allowing flexible scanning coverage based on the complexity of the object. The device incorporates\u0026nbsp;texture mapping capabilities, enhancing the scanned models with realistic surface details. The\u0026nbsp;illumination source consists of a Blue LED light,\u0026nbsp;which helps in reducing interference from ambient lighting and improving scan clarity.\u003c/p\u003e\n\u003cp\u003eThe scanner operates at a\u0026nbsp;frame capture rate of 25 to 30 frames per second, ensuring smooth data collection without lag. Additionally, with a\u0026nbsp;data capture speed of 3 million points per second, the scanner efficiently acquires dense point cloud data for\u0026nbsp;high-resolution 3D models. To enhance processing efficiency,\u0026nbsp;multi-core processing is supported, enabling faster data handling and real-time rendering. The device is lightweight, weighing\u0026nbsp;900 grams (1.9 pounds), making it portable and user-friendly for extended scanning sessions.\u003c/p\u003e\n\u003cp\u003eThe scanner is equipped with a\u0026nbsp;4-inch touchscreen display, allowing for\u0026nbsp;real-time monitoring and parameter adjustments. It comes bundled with\u0026nbsp;dedicated software, facilitating seamless integration with post-processing workflows. The operating temperature between\u0026nbsp;+5\u0026deg;C and +40\u0026deg;C certifies reliable performance across diverse environmental conditions. These technical specifications collectively contribute to the scanner\u0026rsquo;s capability to deliver\u0026nbsp;high-fidelity 3D models, making it a valuable tool for precision-dependent applications such as\u0026nbsp;dentistry, engineering, and medical imaging. Technical Specifications of the 3D Scanner defined in the table 2.\u003c/p\u003e\n\u003cp\u003eTable 2: Technical Specifications of the 3D Scanner\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003eParameter\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eSpecification\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eMeasurement Accuracy\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026plusmn;0.07 mm\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eDistance-Based Accuracy\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026plusmn;0.1 mm per meter\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003ePoint Precision\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026plusmn;0.15 mm\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eScanning Depth Range\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e180 mm - 300 mm\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eMinimum Field of View\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e86 \u0026times; 115 mm\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eMaximum Field of View\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e144 \u0026times; 192 mm\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eTexture Mapping\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eEnabled\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIllumination Source\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eBlue LED Light\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eFrame Capture Rate\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e25 - 30 frames per second\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eData Capture Speed\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e3 million points per second\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eMulti-Core Processing\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eSupported\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eDevice Weight\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e900 grams (1.9 pounds)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eTouchscreen Display\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eYes, 4-inch screen\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eBundled Software\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eIncluded with the scanner\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eOperating Temperature Range\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e+5\u0026deg;C to +40\u0026deg;C\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Calibration of Calibry Mini Nest 3D Scanner\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure accurate data acquisition, the\u0026nbsp;Calibry Mini 3D scanner is calibrated before scanning. The calibration procedure is crucial for minimizing errors in\u0026nbsp;point cloud generation and improving the precision of\u0026nbsp;surface reconstruction. The following steps outline the calibration process:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePreparation of the Calibration Board \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cbr\u003e\u0026nbsp;A factory-provided calibration panel with a predefined pattern is securely placed on a stable surface to maintain a fixed reference. Proper lighting conditions are ensured to avoid reflections or uneven illumination, which may affect calibration accuracy.\u003c/li\u003e\n \u003cli\u003eLaunching Calibration Mode in Calibry Nest \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cbr\u003e\u0026nbsp;The Calibry Nest software is opened, and the scanner is connected to the system. The calibration mode is selected, initiating the process where the scanner recognizes the calibration pattern.\u003c/li\u003e\n \u003cli\u003ePositioning the Scanner at Optimal Distance \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cbr\u003e\u0026nbsp;The scanner is held at the recommended distance, ensuring that the entire calibration board is within the field of view. The software provides real-time feedback, assisting in achieving the correct positioning.\u003c/li\u003e\n \u003cli\u003eCapturing Multiple Angles \u0026nbsp;\u003cbr\u003e\u0026nbsp;The scanner is gradually tilted and rotated to different orientations, allowing it to capture the reference pattern from various perspectives. This step ensures calibration across all possible scanning angles.\u003c/li\u003e\n \u003cli\u003eProcessing and Adjustment\u0026nbsp;\u003cbr\u003e\u0026nbsp;Once the required images are captured, the software processes the calibration data and automatically adjusts internal parameters, including lens distortion correction, depth accuracy, and alignment precision.\u003c/li\u003e\n \u003cli\u003eVerification and Finalization\u003cbr\u003e\u0026nbsp;The system verifies calibration accuracy by comparing the scanned reference with the stored pattern. If deviations exceed the acceptable threshold, recalibration is performed. Upon successful verification, the calibration process is finalized, and the scanner is ready for precise 3D data acquisition.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFigure 5 illustrates the structured workflow of the of calibration of 3D Scanner This calibration procedure is performed periodically or whenever scanning accuracy discrepancies are observed to ensure consistent and reliable results in 3D model generation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Scanning Experimentations and DoE Runs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 6 provides a comprehensive representation of the \u003cstrong\u003e3D scanning workflow\u003c/strong\u003e, outlining key stages involved in the digitization and evaluation process. The workflow begins with the \u003cstrong\u003eexperimental setup for 3D scanning\u003c/strong\u003e, where a structured environment is arranged to ensure precise data acquisition. A Calibry Mini 3D scanner is utilized to capture detailed surface geometries of the dental models under controlled conditions. The next step involves the\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003einterface and functional workflow of Calibry Mini Nest software\u003c/strong\u003e,\u003c/strong\u003e where the scanned data is processed, refined, and converted into a digital format suitable for further analysis.\u003c/p\u003e\n\u003cp\u003eIn the scanning procedure, the typodont model is carefully positioned over a swivel table facilitating controlled rotational motion while maintaining a fixed scanner position. This setup ensures uniform data acquisition from multiple angles without altering the scanner\u0026apos;s placement, thereby improving the precision of the captured model. To regulate the influence of ambient lighting conditions, a light intensity meter is employed to measure and monitor the surrounding illumination, preventing potential distortions in the scanned data. Additionally, the angular orientation of the scanner is determined using a gyroscope-based sensor embedded in a smartphone, which allows for precise adjustments in scanning angles. This systematic approach helps in optimizing the scanning parameters, ensuring high dimensional accuracy and consistency in the digital reconstruction of the dental model.\u003c/p\u003e\n\u003cp\u003eOnce the initial scanning is completed, the \u003cstrong\u003eoptimized 3D scan model is generated after parameter refinement\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e ensuring that the acquired data aligns with the required accuracy standards. Various scanning parameters, such as scanning angle, distance, and illumination settings, are optimized to enhance the fidelity of the final model. The last stage involves a \u003cstrong\u003e3D comparison of the scanned models in Geomagic Control X\u003csup\u003eTM\u003c/sup\u003e software\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e where the accuracy of the scanned model is evaluated by comparing it with a reference dataset. This comparison aids in identifying deviations and optimizing scanning techniques for improved precision.\u003c/p\u003e\n\u003cp\u003eThe interconnected steps in Figure 6 illustrate a \u003cstrong\u003estructured methodology for high-accuracy 3D scanning\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e emphasizing the importance of parameter control, software processing, and accuracy validation in achieving reliable digital reconstructions.\u003c/p\u003e\n\u003cp\u003eThe various scanning parameters and their respective ranges considered in this study are detailed in Table 3. To systematically determine the optimal parameter combinations, the Design of Experiments (DoE) methodology is employed. This approach minimizes redundant trials by focusing on the most influential factors that affect the scanning outcomes. By efficiently analyzing different parameter settings, DoE helps in identifying the critical conditions required to enhance scanning accuracy and model precision. This ensures that the experiments are conducted in a structured manner, leading to improved data reliability and a more efficient scanning process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Different Scanning Parameters and Their Range for both lower and upper denture model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSub Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eScanning Distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNumeric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eScanning Angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNumeric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLight Intensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWatt/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNumeric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 7 illustrates the generated lower dental CAD models using parametric combinations derived through the Design of Experiments (DoE) as per table 4 methodology. A face-centered central composite design was employed to systematically analyze the impact of different scanning parameters on the final 3D reconstruction. Twenty experimental trials were conducted, where each iteration involved scanning a physical lower denture model to obtain its corresponding digital representation. The study controlled key input variables to improve model accuracy and ensure that the scanned data preserved the intricate anatomical details of the denture. The effect of the scanning angle was examined by positioning the scanner at different orientations, ranging from 30\u0026deg; to 105\u0026deg;, to assess its influence on dimensional accuracy. Additionally, light intensity variations between 10 W/m\u0026sup2; and 37 W/m\u0026sup2; were tested to determine the role of illumination in data acquisition. The impact of scanning distance was also evaluated by adjusting the scanner\u0026apos;s position between 20 cm and 60 cm to identify the optimal range for capturing precise surface details. This systematic approach enabled the refinement of scanning parameters, leading to enhanced accuracy and reliability in the generation of lower dental CAD models. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Design of Experiment of lower dental model with corresponding outputs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"424\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScanning Distance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScanning Angle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLight Intensity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScanning Time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.4833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.4677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.4838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.5168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.6353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.6857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.5227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.4656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.5097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.4859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.6692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.4336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.5406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.5137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.4648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.5523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.6699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.4482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 8 illustrates the generated upper dental CAD models by utilizing parametric combinations obtained through the Design of Experiments as per table 5 (DoE) methodology. A face-centered central composite design was implemented to conduct twenty experimental trials, each incorporating different variations in input parameters to assess their influence on scanning precision. During each scan, a physical upper denture model was digitized, and the acquired data was processed to develop an accurate 3D CAD representation. This systematic approach ensures the detailed capture of dental morphology while refining scanning conditions for improved dimensional accuracy and consistency in digital modeling. The study evaluates key scanning parameters, including scanning angle, which varies between 30\u0026deg; and 105\u0026deg; to analyse its effect on dimensional precision. Additionally, light intensity is adjusted within a range of 10 W/m\u0026sup2; to 37 W/m\u0026sup2; to investigate its impact on scan quality. Furthermore, the scanning distance is modified between 20 cm and 60 cm to determine the optimal range for precise data acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Design of Experiment of Upper dental model with corresponding outputs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"424\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScanning Distance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScanning Angle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLight Intensity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScanning Time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.4733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.4509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.5055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.6902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.6809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.5914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.6331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.6974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.6902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.5953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.5634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.5932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.6094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.5223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.7315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.5093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.6096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.4103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eStandard deviation and scanning time are utilized as key quality indicators to evaluate the accuracy and reliability of different parameter sets used in the scanning process. These metrics serve as output responses to determine the influence of scanning parameters, due to their significance in quality control, sensitivity analysis, and statistical validation. The variation in standard deviation for upper and lower dental models across various scanning conditions is depicted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe generated point cloud data includes a standard deviation, indicating the degree of variation from the mean. The accuracy of the scanned model is evaluated by comparing the captured surface data with the reference STL file, which is created based on prescribed scanning protocols and verified through preliminary trials. Standard deviation serves as a measure of scan consistency and precision relative to the mean. To enhance processing efficiency, the data point density should be optimized, ensuring a balance between computational speed and sufficient feature representation. If the scan obtained using the specified parameters demonstrates improved precision and higher quality compared to a scan conducted without specific parameter adjustments, it indicates the effectiveness of the chosen parameters in enhancing scan quality. Conversely, if minimal or no improvement is observed, it may suggest that the selected parameters are unnecessary or that alternative parameters require adjustment to refine the scan output. The efficacy of scanning variables can be assessed by systematically comparing scans with and without parameter adjustments, allowing for further optimization of the scanning process to achieve superior accuracy and reliability. Figures 9a and 9c present the reference data of the lower and upper dental scan model primitive surfaces of the model geometry using different colors within the inspection software, while Figures 9b and 9d illustrate the measured data of the lower and upper dental scan model in various orientations. Additionally, Figures 10a and 10d visualize the initial alignment of lower and upper dental model respectively. Figures 10b and 10e visualize the optimized Best-Fit lower and upper dental scan model respectively. Figures 10c and 10f provide a 3D comparison of the scan models, demonstrating the initial alignment of scan models with reference data.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.4 Model Development\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eGiven the intricate nature of parametric interactions in 3D scanning optimization, traditional empirical methods often struggle to accurately acquire the complex relationships between input parameters and output performance metrics. To overcome this challenge, a machine learning-based modeling approach was employed in this study. Specifically, a Back Propagation Artificial Neural Network (BPANN) was utilized to develop a predictive model capable of mapping nonlinear dependencies between scanning parameters and performance outcomes with high precision.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.4.1 Model Selection Criteria\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe selection of an optimal model was based on two key performance indicators:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eCoefficient of Determination (R\u0026sup2;):\u003c/strong\u003e This statistical measure determines the extent to which the independent variables account for the variation in the dependent variable. A greater R\u0026sup2; value indicates a stronger correlation between predicted and actual values, signifying the model\u0026apos;s ability to accurately represent the data patterns.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRoot Mean Square Error (RMSE):\u003c/strong\u003e RMSE measures the average deviation between predicted and actual values, reflecting the overall accuracy of the model. A lower RMSE signifies better predictive performance, ensuring minimal discrepancies between estimated and observed data points [22].\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy analyzing these performance metrics, the most suitable BPANN model configuration was identified, ensuring a reliable and robust representation of the correlation between input scanning parameters and the resulting scan quality. This model served as the foundation for further optimization, enabling systematic exploration of parameter settings to achieve optimal scanning performance.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.4.2 BPANN Model Architecture and Training\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe BPANN model employed in this study consisted of three primary layers:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eInput Layer:\u003c/strong\u003e Comprising neurons corresponding to the selected 3D scanning parameters, including scanning distance, scanning angle, and light intensity. These parameters were chosen due to their significant influence on scan accuracy and processing time.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHidden Layer(s):\u003c/strong\u003e A set of neurons designed to capture complex nonlinear interactions between input parameters. The number of hidden neurons was optimized through iterative experimentation to accomplish the best trade-off between model accuracy and computational efficiency [23].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOutput Layer:\u003c/strong\u003e Consisting of neurons representing the performance metrics, including standard deviation and scanning time, which were the key optimization objectives in this study.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo enhance learning efficiency and avoid overfitting, the model was trained using the Levenberg-Marquardt algorithm, a widely used optimization technique in neural networks. The dataset was bifurcate into training (70%), validation (15%), and testing (15%) subsets to ensure unbiased performance evaluation.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.4.3 Model Performance Evaluation\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe BPANN model was iteratively trained and tested using a dataset generated from experimental trials. Performance was assessed by comparing predicted values against actual experimental results, evaluating R\u0026sup2; and RMSE values to determine accuracy. The final trained model exhibited a high R\u0026sup2; value (\u0026gt;0.95) and a low RMSE, confirming its effectiveness in predicting scanning outcomes.\u003c/p\u003e\n\u003cp\u003eThe developed BPANN model provides a data-driven framework for analyzing the influence of 3D scanning parameters, enabling researchers and practitioners to fine-tune process settings for optimal scan quality and efficiency. By leveraging machine learning techniques, this approach eliminates the limitations of conventional trial-and-error methods, facilitating precise and reliable parameter optimization for advanced scanning applications [24,25].\u003c/p\u003e\n\u003ch3\u003e3.5 Multi-Objective Optimization Algorithms (NSGA-II)\u003c/h3\u003e\n\u003cp\u003eThe Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is a well-established multi-objective optimization technique, particularly efficient in solving complex engineering problems by balancing competing objectives. As an extension of the traditional genetic algorithm (GA), NSGA-II incorporates key enhancements, including elitism and crowding distance, to ensure robust convergence and solution diversity [26,27].\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.5.1 NSGA-II Implementation in 3D Scanning Optimization\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eIn this study, NSGA-II was utilized to optimize 3D scanning parameters, focusing on scanning distance, scanning angle, and light intensity. These parameters significantly influence the accuracy, standard deviation, and scanning time in 3D scanning processes [28]. The optimization aimed to minimize both standard deviation and scanning time while maintaining high precision in the generated 3D models [27].\u003c/p\u003e\n\u003cp\u003eThe algorithm commences with generating an initial population of candidate solutions, where each individual represents a unique combination of scanning parameters within predefined constraints. The constraints applied in this study were:\u003c/p\u003e\n\u003cp\u003e20 \u0026le; X1 \u0026le; 60\u003cbr\u003e\u0026nbsp;30 \u0026le; X2 \u0026le; 105\u003cbr\u003e\u0026nbsp;10 \u0026le; X3 \u0026le; 37\u003c/p\u003e\n\u003cp\u003eEach candidate solution was evaluated based on its fitness values, which were determined using a dominance-based ranking system [28]. Solutions that outperformed others in at least one objective without being worse in any other were assigned to the first Pareto front [29]. The remaining solutions were assigned to subsequent fronts on the basis of their dominance relationships [26].\u003c/p\u003e\n\u003cp\u003eTo maintain diversity, NSGA-II employs a crowding distance metric, which measures the density of solutions around an individual in the objective space [28]. This ensures that selected solutions are well-distributed across the Pareto front, preventing premature convergence to a local optimum. The crowding distance approach favors solutions in sparsely populated regions, maintaining a diverse set of optimal solutions [27].\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.5.2 Selection, Crossover, and Mutation\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eParents for the next generation were selected based on their Pareto rank and crowding distance [27]. Solutions with lower rank and higher crowding distance were given preference to maintain diversity and solution quality. The selected parents underwent:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eRecombination (Crossover):\u003c/strong\u003e This step involved exchanging genetic material between parent solutions to create offspring that inherit advantageous traits.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMutation:\u003c/strong\u003e Minor random modifications were applied to the offspring to explore unexplored areas of the solution space and avoid early convergence.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo ensure population diversity, a reduction rate (r) of 0.1 was used, limiting the maximum number of solutions from each Pareto front to 10% of the total population size [28]. The new offspring population was then combined with the parent population, and a new generation was formed using a survivor selection strategy based on non-dominated sorting and crowding distance [30].\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3.5.3 Convergence and Stopping Criteria\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eNSGA-II continued the optimization process iteratively until a predefined stopping criterion was met [28]. This included:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eMaximum Number of Generations:\u003c/strong\u003e The algorithm was allowed to run for a set number of generations to explore the solution space sufficiently.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConvergence of Pareto Front:\u003c/strong\u003e If the Pareto front solutions stabilized and no significant improvements were observed in successive generations, the optimization process was terminated.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe final Pareto-optimal solutions represent a set of trade-offs between scanning accuracy and scanning time, providing a comprehensive view of the best possible parameter configurations for 3D scanning [29]. These solutions offer decision-makers insights into the optimal balance between minimizing standard deviation and reducing scanning time, ensuring high-quality 3D models with efficient scanning performance [30].\u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eThe analysis of the artificial neural network (ANN) provides valuable insights into the performance and predictive capabilities of the proposed model for optimizing 3D scanning parameters. The two-output neural network structure, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e, demonstrates high accuracy in predicting scanning outcomes based on key input variables, such as scanning distance, scanning angle, and light intensity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003ea and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eb illustrate that the model underwent periodic evaluations to maintain reliability and avoid excessive training. For the lower dental model, the ANN model attained a training R-value of 0.99965, demonstrating a strong correlation with the training dataset, while the testing R-value of 0.99955 indicates effective generalization to new data. Additionally, the validation R-value of 0.99971 highlights the model\u0026rsquo;s stability when handling new datasets. The overall R-value of 0.99961, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003ea, confirms the strong predictive performance across all datasets. Similarly, for the upper dental model, the ANN yielded a training R-value of 0.99986, a testing R-value of 0.99919, and a validation R-value of 0.99964, resulting in an overall R-value of 0.99968 as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eb. These consistently high R-values demonstrate the effectiveness of the model in capturing complex relationships between 3D scanning parameters and output metrics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, the model\u0026rsquo;s training efficiency is evident in the mean squared error (MSE) values, with the lowest MSE of 1.1414 achieved at epoch 3 for the lower dental model (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e14\u003c/span\u003ea) and 2.5992 at epoch 3 for the upper dental model (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e14\u003c/span\u003eb). This indicates that the network reached optimal performance early in the training process, minimizing errors between predicted and actual values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe error histogram, as presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003ea and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003eb for the lower and upper dental models respectively, provides additional validation of the model\u0026rsquo;s accuracy. The histogram divides errors into 20 discrete bins, revealing that the training error is concentrated around an error value of 0.03856 for the lower dental model and 0.07397 for the upper dental model. The zero-error line in the histogram further reinforces the model\u0026rsquo;s precision, with most errors clustered close to zero, suggesting minimal deviation from actual values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, the gradient analysis highlights key convergence characteristics of the model. A small gradient value of 0.0087092 at epoch 9 for the lower dental model (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e16\u003c/span\u003ea) and 0.0024936 at epoch 9 for the upper dental model (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e16\u003c/span\u003eb) suggest that the model approached a point of diminishing returns in learning, ensuring stable optimization. The low Mu value of 1\u0026times;10⁻⁷ at epoch 9 for both models indicates that the network made cautious weight updates, preventing overfitting while fine-tuning parameter relationships. The validation checks of 6 at epoch 9, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003ea and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eb, confirms that the model was periodically assessed to ensure reliability and prevent unnecessary overtraining. The training process concluded once the validation criteria were met, achieving optimal predictive performance.\u003c/p\u003e\u003cp\u003eThe proposed NSGA II-ANN optimization framework was employed to investigate the influence of scanning distance, scanning angle, and light intensity on standard deviation and scanning time in 3D scanning models. Through experimental trials, critical process parameters were identified, confirming that integrating machine learning algorithms with 3D scanning optimization enables substantial reductions in scanning time while improving dimensional accuracy. The Pareto front solutions, presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e17\u003c/span\u003ea and \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e17\u003c/span\u003eb, illustrate the trade-offs between competing objectives, guiding the selection of optimal scanning conditions. Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows Pareto front values with corresponding outcomes of lower and upper dental models respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePareto front values with corresponding outcomes of lower dental model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScanning Distance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScanning Angle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLight Intensity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eScanning Time\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.9056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.2818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.6236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8526\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.3902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.0197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.1778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.9299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.7985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.6955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.7144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.3543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.0181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.7828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.9036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.3711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.2964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.5255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.3982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.9638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.0573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.3197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8812\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.5506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.5624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.6431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.7743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.9965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.0464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.5732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.7783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.5983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.3016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.7435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.3453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.3307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.7869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.9178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.9494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.9017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.8035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.1278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.0872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.5605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.3245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.0792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.9568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.0877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.7119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.1045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.7088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.7332\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.6364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.4093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.2948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.9438\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.1076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.8372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.3367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.0111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.8361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.8076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.7782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.0322\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.0982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.2895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.0452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.9169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.7889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.8705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.2708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.3789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.8929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.2429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.8127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.896\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.0621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.0335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.0493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.4024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.9867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.2099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.6389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.3792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.2227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.9732\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.8072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.3097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.9015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.1722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8332\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.0523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.0321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.4213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.9857\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.1299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.0786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.8345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.5371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.0464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.7999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.5599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.0095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.0746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.0724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.5053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.9258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResult validation of lower dental model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSr. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScanning Distance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScanning Angle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLight Intensity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePredicted Standard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExperimental Standard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e% Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePredicted Scanning Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eExperimental Scanning Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e% Error\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.5053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.9997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e87.9258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePareto front values with corresponding outcomes of Upper dental model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScanning Distance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScanning Angle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLight Intensity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eScanning Time\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.1062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.0564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.2729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.4403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.8848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.5229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.5038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.2605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.6045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.8606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.7823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.8074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.7992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.1062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.3064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.2353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.6227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.5241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.6219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.5378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.6857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.752\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.4665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.9402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.6448\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.8616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.5296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.5762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.9875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.8801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.4674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.6594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.3085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.8836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.7861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.0092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.7729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.6739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.7406\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.2833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.8777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.9119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.3294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.0867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.6001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.0953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.3943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.4481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.0548\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.3553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.1301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.5504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.3566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.7321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.7748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.1616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.3102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.5564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.4235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.4557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.5639\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.7628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.4821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.6943\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.4385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.7294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.7655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.5877\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.3078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.1397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.9823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.8464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.7083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.6771\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.4738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.5191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.7059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.8548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.3849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.6733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.9608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.5967\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.6062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.3064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.1848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.9344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.9777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.0658\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.6791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.4483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.1062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.0564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.2729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.5187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.8948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.0337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.2823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.8074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87.8312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResult validation of upper dental model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSr. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScanning Distance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScanning Angle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLight Intensity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePredicted Standard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExperimental Standard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e% Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePredicted Scanning Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eExperimental Scanning Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e% Error\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.2823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.8074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.9419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e87.8312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe optimal scanning parameters identified for the lower dental model included a scanning distance of 23.8 cm, a scanning angle of 45.5053\u0026deg;, and a light intensity of 31.9997 W/m\u0026sup2;. For the upper dental model, the best results were obtained with a scanning distance of 20.2823 cm, a scanning angle of 48.8074\u0026deg;, and a light intensity of 31.9419 W/m\u0026sup2;. Validation experiments, as presented in Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, demonstrated a strong correlation between the predicted and actual values, with minimal deviations observed in standard deviation and scanning time.\u003c/p\u003e\u003cp\u003eThe results underscore the impact of scanning parameters on accuracy and efficiency, offering practical guidance for optimizing 3D scanning workflows in dental applications. By leveraging NSGA II-ANN optimization, manufacturers and researchers can enhance the precision and reliability of dental scanning processes, ensuring high-quality outputs that meet clinical and industrial standards. Furthermore, the integration of advanced scanning techniques with AI-driven parameter tuning paves the way for improved fabrication of dental devices, contributing to personalized healthcare solutions and next-generation medical imaging technologies.\u003c/p\u003e"},{"header":"5. Conclusions and Future Scope","content":"\u003cp\u003eThe proposed research proficiently bridges the gaps of advanced digital imaging, computational optimization, and the pressing realities of global oral health, has yielded compelling insights into the critical role of scanning parameter optimization in maximizing the efficacy of handheld dental 3D scanners. The study concluded that subtle adjustments in parameters such as scanning distance, scanning angle and light inytensity can yield statistically significant improvements in both the accuracy of the digital models and the efficiency of the acquisition process. This optimization directly translates to enhanced diagnostic fidelity, particularly crucial in the early detection of conditions like dental caries affecting a vast majority of the global pediatric population, and improved clinical workflows, essential as the burden of periodontal diseases continues its upward trajectory, reaching a larger section of the global population. The implications of this research extend beyond the specific handheld scanner investigated. The MOGA-ANN optimization framework we have developed offers a generalizable paradigm for the systematic evaluation and refinement of scanning protocols across various dental 3D imaging technologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors confirm that this study has no financial or personal conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical clearance: NA\u003c/strong\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026bull;Sumit Gahletia: Conducted data collection and analysis, drafted the original manuscript, and contributed to visualization.\u0026bull;Ramesh Kumar Garg: Provided conceptualization, supervision, methodological guidance, and critically reviewed the original draft.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors sincerely appreciate the support extended by DST INSPIRE for funding assistance and DCRUST for providing access to laboratory facilities essential for this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eS. 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Computat Methods Eng. \u003cb\u003e29\u003c/b\u003e, 2285\u0026ndash;2308 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11831-021-09663-x\u003c/span\u003e\u003cspan address=\"10.1007/s11831-021-09663-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biomedical-materials-and-devices","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Biomedical Materials \u0026 Devices](https://link.springer.com/journal/44174)","snPcode":"44174","submissionUrl":"https://submission.springernature.com/new-submission/44174/3","title":"Biomedical Materials \u0026 Devices","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"3D Scanning, patient specific retainer, Dimensional Accuracy, Optimization, NSGA-II, Digital Dentistry","lastPublishedDoi":"10.21203/rs.3.rs-7145046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7145046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile the dental 3D scanner market is projected to surge towards a \u003cspan\u003e$\u003c/span\u003e2.61\u0026nbsp;billion valuation by 2032, with a robust 9.1% compound annual growth rate (CAGR), the fundamental accuracy underpinning its diagnostic promise is crucial for addressing the oral health of nearly 3.5\u0026nbsp;billion individuals, which remains intrinsically tied to the often-overlooked optimization of its scanning parameters. Building upon this confluence of rapidly increasing technology and pressing oral health crisis, proposed research aims to optimize the process parameters of a handheld 3D scanner for accurate and expedite scanning of patient-specific denture models. Scanning experiments are performed at the parametric combination of scanning parameters (scanning speed, angular orientation, and light intensity) is retrieved using the design of experiments methodology for desired output responses (standard deviation and scanning time).\u003c/p\u003e\u003cp\u003eFurthermore the present research employ the potential of metaheuristic optimization algorithms, specifically an implementation of the NSGA-II (Non dominating sorted genetic algorithm) framework. The Artificial Neural Network model trained on an initial dataset of scan runs, to predict the accuracy and scan time across the parameter space, thereby significantly reducing the computational cost associated with exhaustive experimental trials. Subsequently, the Multi-Objective Genetic Algorithm will leverage this trained ANN to efficiently explore the Pareto front, identifying the non-dominated solutions that represent the optimal trade-offs between scanning accuracy, quantified through rigorous metrological analysis comparing the digital models to the standard deviation and scanning time. The primary emphasis of this research is to establish a scientifically validated, data-driven protocol for optimizing dental 3D scanning, thereby ensuring that this transformative technology realizes its full potential in delivering precise, efficient, and ultimately, improved patient care on a global scale.\u003c/p\u003e","manuscriptTitle":"Metrological Analysis and Multi objective optimization of 3D Scanning Parameters for precise scanning of patient-specific dental models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 05:51:42","doi":"10.21203/rs.3.rs-7145046/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-12T23:42:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T14:23:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T05:24:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268990668233112455430379538350255770163","date":"2025-07-30T05:10:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-30T02:48:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1445737663317381569707911392483069702","date":"2025-07-28T12:39:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32210063631530180859545706191624197494","date":"2025-07-23T14:07:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319967090431795633152694782088210232659","date":"2025-07-23T13:14:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T13:04:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T14:36:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T14:31:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biomedical Materials \u0026 Devices","date":"2025-07-17T05:08:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biomedical-materials-and-devices","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Biomedical Materials \u0026 Devices](https://link.springer.com/journal/44174)","snPcode":"44174","submissionUrl":"https://submission.springernature.com/new-submission/44174/3","title":"Biomedical Materials \u0026 Devices","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"df75681c-007a-4ca5-94c7-084b320c7b5b","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T07:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 05:51:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7145046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7145046","identity":"rs-7145046","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}