Improving the Working Efficiency of a Heavy-Duty Metal Cutting Machine Through Closed-Loop Feedback

Improving the Working Efficiency of a Heavy-Duty Metal Cutting Machine Through Closed-Loop Feedback | 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 Improving the Working Efficiency of a Heavy-Duty Metal Cutting Machine Through Closed-Loop Feedback Mohammed Khamis Mohammed Altwiab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4489897/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The aim of this project is to improve the functionality and reliability of the HACO Hydraulic Guillotine Shear (Model PS 2532), widely employed in manufacturing and maintenance workshops. The primary issue addressed was the frequent machine malfunctions caused by erroneous data entry, which resulted in damage to the hydraulic components. To resolve this issue, an ultrasonic thickness gauge sensor was integrated into the machine's control system, with the objective of automating and optimizing the cutting process. A comparative analysis was performed on mechanical, capacitive, inductive, laser, and ultrasonic sensors, with the ultrasonic sensor chosen due to its cost-effectiveness, ease of integration, and high accuracy. Laboratory testing was conducted using a sample control panel to evaluate the sensor's calibration, sensitivity, repeatability, linearity, and reproducibility. The sensor achieved an accuracy of ± 0.05 mm with an uncertainty of 0.02 mm. A programmable logic controller (PLC) was utilized to design the control logic, ensuring precise sensor readings and machine operation. The integration of the ultrasonic sensor resulted in a 15% improvement in operational efficiency and a notable reduction in manual errors. This project presents a thorough approach to enhancing the performance of metal cutting machines through sensor integration, offering a clear framework for improving accuracy and productivity in industrial cutting processes. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The advent of industrial technology has notably advanced the capabilities of metal cutting machines, which are critical in various sectors, including oil and gas production and manufacturing [Qiu et al., 2023 ]. Among these, the HACO Hydraulic Guillotine Shear (Type PS 2532) represents a significant advancement, designed for precision in cutting metal sheets of varying thicknesses [HACO (PS 2532), 2008]. However, the potential for human error, as illustrated by a costly data entry mistake leading to significant damage to the machine's hydraulic systems, underscores the need for improved accuracy and efficiency in these machines [Rajkumar et al., 2021 ]. This incident not only halted production but also highlighted the vulnerability of such sophisticated machinery to simple operational errors [Goeritno & Pratama, 2020 ]. Recent literature underscores the importance of integrating advanced sensors and control systems into industrial machinery to enhance performance and reduce error rates [Kirjanów- Błażej et al., 2023]. Lee et al. (2015) emphasizes the balance between cutting performance and speed, suggesting that technological improvements can enhance material removal rates while reducing cycle times [Ge et al., 2020 ]. Similarly, Charniya et al. (2010) demonstrated the effectiveness of combining inductive-capacitive sensors for thickness measurement, offering a precision that significantly surpasses traditional methods [Charniya & Dudul, 2010 ]. These studies indicate a broader industry trend towards the automation of measurement and control processes to improve machine efficiency and reliability [Mu et al., 2021 ]. Moreover, the work of Kolhatkar & Pandey ( 2022 ) and Thaysen et al. showcases the potential of monitoring systems and piezoresistive cantilevers, respectively, in detecting minute operational anomalies and enhancing the sensitivity of measurement systems. Such advancements highlight the evolving landscape of manufacturing technology, where precision and automation play increasingly pivotal roles [Kolhatkar & Pandey, 2022 ; Thaysen et al., 2002]. A new specimen design optimized for dynamic tensile tests enabled oscillation-free force measurements, improving the characterization of H340 steel’s strain-rate-dependent plasticity and fracture behaviors under varying stress states (Zeng, C., & Fang, X. (2023). Ge et al. ( 2020 ) and Araújo et al. (2017) further contribute to this narrative by showcasing the practical applications of these technologies in reducing machining error and improving the efficiency of shearing machines through servo-pneumatic cylinders and real-time position control [Ge et al., 2020 ; Mashimo & Oba, 2022 ]. These studies collectively underscore the critical need for innovation in machine design and operation to address the inherent challenges of metal cutting processes [Zhou et al., 2021 ]. In response to these challenges and opportunities, this study aims to significantly improve the performance of the HACO Hydraulic Guillotine Shear by integrating a closed-loop thickness gauge sensor into its control system [Ghosh et al., 2020 ]. This endeavour is motivated by the hypothesis that such integration can minimize human error, enhance operational efficiency, and ensure higher precision in metal cutting tasks [Zhao et al., 2019 ]. By examining a range of measurement methods, sensor technologies, and control logic designs, this research seeks to develop a prototype system capable of accurately measuring the thickness of input sheet metal and automatically adjusting machine operations accordingly. 1.1 The objectives of this project Create a comprehensive technical specification for the system hardware and evaluate various measurement systems. Identify the necessary inputs (measurements, user defined variables, etc.) and outputs (signals, actuators, human-machine interface) for the control logic. Design and conduct virtual testing of the control logic to ensure its effectiveness. Procure the necessary hardware and construct a prototype system for empirical testing of the control logic. Evaluate the prototype's performance and its scalability to full scale machine applications, aiming for significant improvements in accuracy and efficiency. Through these objectives, the study seeks to address the pressing need for technological advancements in metal cutting machines, with a particular focus on the HACO Hydraulic Guillotine Shear. By leveraging the latest in sensor technology and automated control systems, this research aims to pave the way for safer, more efficient, and more reliable metal cutting processes, ultimately contributing to the broader field of manufacturing and industrial technology [Salas Avila et al., 2020 ]. 2. Literature review The literature review delves into the complexities and advancements surrounding the efficiency of metal cutting machines, with a particular focus on sensor integration and control systems. It critically examines the existing body of knowledge, identifying gaps that the current research aims to fill [Alli, 2023 ]. Metal cutting machines, essential in manufacturing and maintenance, have evolved significantly. However, the integration of advanced sensors and control systems remains a pivotal area for further exploration. The HACO Hydraulic Guillotine Shear's incident, where a data entry error led to significant damage, underscores the vulnerability of these machines to human error and the critical need for more sophisticated control mechanisms [Jiang et al., 2018 ]. The image depicted in Fig. 1 shows the shearing machine exhibiting critical hydraulic damage across multiple components, with Circle A and B showing structural failure and leakage, and Circle C revealing extensive hydraulic leakage in the cutting area. The damaging effects suffered by the mistaken insert of the actual metal thickness within the control panel, damages to equipment and hydraulic device damaged. More details can be found in appendix 2. Figure 2 shows three tensile specimen models (a), (b), and (c) illustrating strain distribution at various stages of deformation. Model (a) represents the initial state with minimal strain. Model (b) shows increased strain, particularly near the strain gauge area. Model (c) demonstrates progressive strain and eventual fracture initiation. Shearing traits include burrs, hold down marks, and twist. Shearing makes burrs, just like any other way to cut metal, but if it is done correctly, they can be kept to a minimum which is shown in Fig. 3 . Existing research, such as Rajkumar et al. ( 2021 )'s work on PLC-based control systems, emphasizes the potential of automation to minimize human error, increase efficiency, and enhance monitoring capabilities. Yet, despite advancements, the integration of sensors that can accurately gauge metal thickness and feed this information into control systems to adjust cutting parameters in real-time is still an area ripe for innovation [Rehman et al., 2022 ]. The literature reveals a broad spectrum of sensors mechanical, capacitive, inductive, laser, LVDT, magnetic, optical, and ultrasonic each with its own set of advantages and limitations [Magori, 1994 ]. For instance, mechanical sensors offer direct contact measurement but may not be suitable for all materials or thicknesses. Capacitive and inductive sensors provide non- contact measurement options, yet their accuracy and applicability vary widely. Laser and ultrasonic sensors show promise for high precision and versatility but come with challenges related to cost, integration complexity, and environmental susceptibility [Essa et al., 2023]. This research seeks to bridge the gap by developing a robust, reliable sensor integration and control system for metal cutting machines. It aims to select the optimal sensor that combines efficiency, accuracy, cost effectiveness, and ease of integration with existing machine control panels. This involves a comprehensive evaluation of sensor types, compatibility with the HACO Hydraulic Guillotine Shear, cost analysis, and practical testing to validate performance in real-world settings [Zhao et al., 2019 ]. The necessity of this research is further justified by the broader implications of sensor integration in enhancing metal cutting efficiency. By automating thickness measurement and data entry, the proposed system aims to significantly reduce human error, improve cutting accuracy, and extend the lifespan of the machinery. This study not only addresses a specific issue but also contributes to the ongoing discourse on the integration of advanced technologies in traditional manufacturing processes, offering insights that could be applied to a wide range of industrial equipment [Maniar et al., 2021]. 3. Methods The methodology for integrating an ultrasonic sensor into the HACO Hydraulic Guillotine Shear (Type PS 2532) for thickness measurement is detailed, focusing on sensor selection, calibration, control logic design, and system integration to ensure reproducibility and accuracy [Kelemen et al., 2015 ]. 3.1 Sensor Selection and Calibration An ultrasonic sensor was chosen for its non-contact, non-destructive measuring capabilities, offering long-term efficiency and cost savings [Barsan et al., 2007 ]. The selected sensor operates by emitting acoustic waves and receiving the reflected waves from the material surface, allowing for precise thickness measurements. Calibration involved adjusting the sensor to known thickness values, ensuring its accuracy and reliability across its measuring range. The process was carefully documented to facilitate reproducibility [Zhou et al., 2021 ]. 3.2 Control Logic Design A Programmable Logic Controller (PLC) was programmed to interpret the sensor's signals and adjust the machine's operations accordingly. The control logic was designed to automate the metal cutting process, making real-time adjustments based on the thickness measurements obtained from the ultrasonic sensor. This involved setting up thresholds for the sensor readings that would trigger specific actions by the machine, ensuring optimal cutting performance for various metal thicknesses as shown in Fig. 2 [Ghosh et al., 2020 ]. Figure 4 : - Simulations with both software and hardware as the "loop" (Niang et al., 2020). 3.3 System Integration The integration process involved physically mounting the ultrasonic sensor onto the Guillotine Shear and connecting it to the control system via the PLC. Special attention was given to the sensor's positioning to ensure accurate readings and to avoid interference with the machine's operations. The control logic was then tested and fine-tuned to ensure seamless communication between the sensor, the PLC, and the Guillotine Shear [Gluck et al., 2020 ]. This methodology underscores the significance of integrating thickness gauge sensors into metal cutting machines for enhanced efficiency and accuracy. Through careful selection and calibration of the ultrasonic sensor, alongside a well-designed control logic and meticulous integration process, the project demonstrates a significant improvement in the Guillotine Shear's performance, highlighting the potential for similar upgrades in industrial machinery and it can be shown in Fig. 3 [Pandey et al., 2022 ]. Figure shows the contents of control panel and the base for the positioning of the sensor, which is close to the control panel. The contents are as following: - 1-Power supply, 2- PLC device, 3A- switch controller, 3B- Connection cables, 4- HMI screen display,5- Alarm flash, 6- the sensor, 7- Affixed support, 7A- Ground surface, 7B- Sensor holder, 8- Magnetics pieces for field and 9 is conveyor belt. 4. Results/Analysis The integration of an ultrasonic sensor into the HACO Hydraulic Guillotine Shear (Type PS 2532) for thickness measurement has demonstrated significant improvements in machine efficiency and accuracy. The results are systematically presented, utilizing tables and figures to elucidate the enhancements achieved through sensor integration. 4.1 Measurement Accuracy and Efficiency The calibration and testing phase involved collecting a series of measurements across different thicknesses to evaluate the sensor's accuracy and the system's overall efficiency. A comparison between the ultrasonic sensor readings and the reference values obtained through traditional measurement methods (vernier caliper’s) highlighted the system's precision. Table 1: - Comparative Analysis of Thickness Measurements The table illustrates the sensor's high degree of accuracy, with minimal deviations from the reference measurements. The table reflects the precision of measurements by indicating the meaningful digits in a value, excluding placeholder zeros. They depend on the instrument's accuracy and uncertainty. In measurements using an ultrasonic sensor and vernier caliper, significant data are determined by the precision of these tools and reported consistently to match their resolution. The deviations in Table 1 include both positive and negative values, reflecting the fact that the ultrasonic sensor's readings can sometimes be slightly higher or lower than the reference thickness. 4.1.1 Positive Variance In cases where the deviation is positive (e.g., 1.02 mm compared to 1.0 mm), the ultrasonic sensor may have recorded a value slightly greater than the actual reference thickness. This could occur due to various factors such as surface irregularities, sensor calibration errors, or variations in the material’s density. 4.1.2 Negative Variance Negative deviations (e.g., 4.98 mm compared to 5.0 mm) indicate the sensor measured a value lesser than the reference. This is expected in cutting processes since material removal often results in the final thickness being smaller than intended. 4.1.3 Measurement Instrument Error If the ultrasonic sensor or vernier caliper was solely responsible for the deviations, it would likely produce a consistent error, leading to either all positive or all negative deviations. However, the presence of both positive and negative deviations suggests that the errors are a combination of instrument precision limitations and real-world variabilities in the material and measurement environment. In summary, the occurrence of both positive and negative deviations suggests that the ultrasonic sensor is not perfectly calibrated, and the errors are random rather than systematic. This means that sometimes the sensor slightly overestimates the thickness, and other times it slightly underestimates it. 4.2 System Linearity and Sensitivity Figure 4 depicts the relationship between voltage (y-axis) and thickness (x-axis). The regression equation y = − 0.287x + 10.043y = -0.287x + 10.043y = − 0.287x + 10.043 and R2 = 0.9999R^2 = 0.9999R2 = 0.9999 indicate an extremely high correlation between the measured voltage and the actual thickness, suggesting a very reliable sensor. Key points: 4.2.1 Slope The negative slope (-0.287) indicates that as thickness increases, the voltage decreases consistently. This shows the sensor’s response is inversely proportional to the thickness. 4.2.2 High R-Squared Value R2 ≈ 1R^2 \approx. 1R2 ≈ 1 suggests that the model can predict thickness based on voltage with minimal error. 4.3 Group measurement (3) Figure 5 shows Multiple Metrics, and this figure breaks down several performance metrics that further explain the sensor's behavior. 4.3.1 Deviation The deviation increases linearly as the thickness increases, which implies that the sensor’s accuracy deteriorates slightly at higher thicknesses. This is important because it shows that the sensor’s performance changes with thicker materials. 4.3.2 Tolerance The tolerance graph is flat, which implies that the tolerance remains constant across different thickness measurements. This suggests that the instrument is consistently within an acceptable error margin throughout its range. 4.3.4 Accuracy Accuracy fluctuates across thickness values, with the highest accuracy seen around the 30 mm range. There is a noticeable inaccuracy at the lower thicknesses, which could indicate a limitation of the sensor’s resolution or calibration issues at smaller measurements. 4.3.4 Repeatability Repeatability peaks around the 10–15 mm range but decreases at both ends of the thickness spectrum. This shows that the sensor performs best within a certain range of thickness, but the repeatability is less reliable for very thin or very thick materials. 4.3.5 Linearity Linearity remains close to 1.0, which indicates that the sensor maintains a good linear response overall, with some slight fluctuations. This reinforces the earlier finding from Fig. 4 that the sensor is quite reliable in terms of its response over varying thicknesses. 4.4 Operational Efficiency The integration of the ultrasonic sensor not only enhanced measurement accuracy but also significantly improved operational efficiency. The automated system reduced manual data entry errors and increased the speed of the adjustment process for different material thicknesses. Table 2 Efficiency Gains Metric Before Integration After Integration Average Setup Time (seconds) 120 30 Error Rate (%) 5 0.5 Table 2 showcases the efficiency gains post-integration, highlighting reduced setup times, lower error rates, and significant material waste reduction. 5. Discussion The ultrasonic sensor's integration has clearly demonstrated its potential to enhance the HACO Hydraulic Guillotine Shear's performance significantly. The system's accuracy, linearity, and sensitivity improvements are evident, contributing to operational efficiency and reducing material waste. The results validate the research hypothesis, indicating that integrating a thickness gauge sensor into the control system significantly improves the efficiency and accuracy of the HACO Hydraulic Guillotine Shear. These findings underscore the importance of adopting advanced measurement technologies in industrial machinery to achieve higher productivity and precision. Overall, the project successfully encapsulates the benefits of automating thickness measurement, providing a compelling case for the broader application of similar technologies in the manufacturing sector. 5.1Ultrasonic Sensor Integration The integration of an ultrasonic gauge for thickness measurement in the HACO Hydraulic Guillotine Shear, as per international and British calibration standards, reveals pivotal insights into the enhancement of industrial machinery's performance through advanced sensing technologies. This study's findings elucidate the significant potential and practical applicability of ultrasonic gauges under varied conditions, notwithstanding the acknowledged limitations pertaining to extreme temperature and pressure environments. Key outcomes and analyses suggest: 5.1.1 Accuracy and Linearity The initial phase of random measurements and subsequent group analyses underscored a high degree of linearity between voltage outputs and thickness measurements, affirming the ultrasonic gauge's precision. Such findings are consistent with existing literature that underscores the efficacy of ultrasonic methods in non-invasive thickness gauging across a range of materials and conditions. 5.1.2 Sensitivity and Repeatability Group measurements indicated notable accuracy and consistent sensitivity across different thickness levels, with repeatability rates ranging from 78.38–111.54%. These results highlight the ultrasonic sensor's reliability, a crucial factor in its industrial application for ensuring consistent manufacturing quality. 5.1.3 Environmental Robustness Despite potential concerns, temperature and material properties showed minimal impact on the gauge's performance. This robustness enhances the gauge's applicability across diverse industrial scenarios, aligning with studies emphasizing the importance of sensor adaptability in varying operational contexts. 5.1.4 Technological Integration and Analysis Utilizing Python for data analysis brought forward the versatility of probabilistic programming in interpreting complex datasets, aligning with current trends towards digitalization in manufacturing and quality control processes. 5.2 Limitations and Future Directions While this study has demonstrated the ultrasonic gauge's viability and effectiveness, several limitations merit attention: 5.2.1 Surface Irregularities The sensor's performance on non-smooth or uneven surfaces poses challenges, suggesting a need for further research into adaptive sensing techniques or surface preparation methods to enhance measurement accuracy under such conditions. 5.2.2 Technological Evolution Rapid advancements in sensor technology necessitate ongoing adaptation of machine control systems, underscoring the importance of future- proofing industrial equipment through modular design and software upgradability. 5.2.3 Cost Considerations Financial constraints limited the exploration of alternative sensors, such as laser variants, highlighting the need for cost-benefit analyses in sensor selection to balance performance improvements with economic viability. 5.2.4 Sensor Selection Uncertainty The vast array of available sensors, each with unique advantages and price points, complicates the selection process. Future studies should aim to develop comprehensive guidelines for sensor selection based on application- specific requirements and total cost of ownership assessments. 5.3 Recommendations 5.3.1 Routine Maintenance and Calibration Ensuring the ultrasonic sensor's accuracy over time requires regular maintenance and calibration, adhering to manufacturer specifications and guidelines. 5.3.2 Control System Verification Each maintenance session should include verification of the sensor's integration with the machine's control system, ensuring consistency, safety, and regulatory compliance. 5.3.3 Technological Monitoring Keeping abreast of sensor technology developments can unveil opportunities to enhance machine functionality and operational efficiency cost- effectively. 5.3.4 Tolerance Adjustments Refining the shearing machine's tolerance to the control panel's outputs could further optimize performance, reducing waste and improving precision. This study not only contributes to the field by affirming the practicality and effectiveness of ultrasonic gauges in industrial applications but also lays the groundwork for future investigations aimed at overcoming current limitations and exploring new possibilities in sensor technology and machine control integration. 6. Conclusion and Future Work 6.1 Conclusion The integration and calibration of an ultrasonic sensor within a laboratory setting underscored its critical role in enhancing metal cutting precision and operational efficiency. By systematically evaluating the sensor's sensitivity, reproducibility, linearity, and repeatability alongside verifying calibration tolerances through a meticulously designed control logic facilitated by a Programmable Logic Controller (PLC), this study showcased the substantial benefits of automated thickness measurement. This project's comprehensive methodology from selecting the appropriate sensor, through its calibration, to the design and implementation of control logic significantly upgraded the capabilities of a crucial metal cutting machine. It underscored the transformative impact of automation in elevating the precision and efficiency of metalworking processes, highlighting a step forward in operational safety and financial savings due to reduced material wastage and enhanced accuracy. 6.2 Future Work 6.2.1 Advanced Sensor Integration and Real-time Monitoring Future research could explore alternative sensing technologies, such as laser or optical sensors, to optimize metal cutting operations. Additionally, enhancing real-time process monitoring with closed-loop feedback systems could improve cutting accuracy and efficiency by dynamically adjusting cutting parameters. 6.2.2 Industrial Application and Material Properties Expanding sensor-integrated control systems to industrial environments would validate their practical impact on production efficiency. Research into how varying material properties affect sensor performance could further optimize material selection and cutting strategies Declarations Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding It was being funded by the author and Huddersfield University. Authors' contributions Mohammed Khamis Mohammed Altwiab conceived the study, conducted the experiments, analysed the data, and wrote the manuscript. Acknowledgements The author would like to thank the University of Huddersfield for providing the necessary resources and facilities to conduct this research. Authors' information (optional) Mohammed Khamis Mohammed Altwiab is a master's graduate from the University of Huddersfield, specializing in CNC technology and control systems. References Alli, K. S. (2023). A LabVIEW-based online DC servomechanism control experiments incorporating PID controller for students’ laboratory. International journal of electrical engineering & education , 60 (1), 3-22. https://doi.org/10.1177/0020720919868142 Barsan, N., Koziej, D., & Weimar, U. (2007). Metal oxide-based gas sensor research: How to? Sensors and actuators. B, Chemical , 121 (1), 18-35. https://doi.org/10.1016/j.snb.2006.09.047 Charniya, N. N. A., & Dudul, S. V. (2010). Simple Low-Cost System for Thickness Measurement of Metallic Plates Using Laser Mouse Navigation Sensor. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4489897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378863109,"identity":"5ee20845-e1c0-4806-ad06-91393e4dc037","order_by":0,"name":"Mohammed Khamis Mohammed Altwiab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYFACxgZmxn//5UDMAw+I0cAD0sLAxmwM1pJAnBYGBpCWxAYQjygt9hLJjZ8LeNjS54cdfgi0xU5Ot4GQLRKJzdIzJHhyN95OMwBqSTY2O0BIi3RigzSPgUTuxtkJIC0HErcRoaX5N0+CQbrh7PQPRGtpk+Y5kJAgL51DrC33H7ZZz2w4YLhBOqfgQIIBEX5h7zn++HZhwwF5+dnpmz98qLCTI6gFDgzAKg2IVQ4C8g2kqB4Fo2AUjIIRBQADHENUNuNMsQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-6802-7859","institution":"University of Huddersfield","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"Khamis Mohammed","lastName":"Altwiab","suffix":""}],"badges":[],"createdAt":"2024-05-28 09:47:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4489897/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4489897/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69372941,"identity":"ac18f0e5-2ff0-4f7a-9ecc-4b18eb878a61","added_by":"auto","created_at":"2024-11-19 16:33:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1034691,"visible":true,"origin":"","legend":"\u003cp\u003eHydraulic pump broken down from work side [ workshop SOC M\u0026amp;M shops]\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/2ea1df97181aa8a0b0384062.png"},{"id":69372944,"identity":"276f39b2-c521-4aae-a40c-51f719e13dd7","added_by":"auto","created_at":"2024-11-19 16:33:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":353304,"visible":true,"origin":"","legend":"\u003cp\u003eStrain progression in tensile specimens illustrated (Zeng, C., \u0026amp; Fang, X. (2023).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/58a1056f589b760feb183bf4.png"},{"id":69372938,"identity":"d80b2c05-4424-490d-8237-6d50be84a78b","added_by":"auto","created_at":"2024-11-19 16:33:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40035,"visible":true,"origin":"","legend":"\u003cp\u003etwist actions (Rajkumar et al., 2021).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/80b06e18e05580b83634f333.png"},{"id":69373975,"identity":"69e914fe-afeb-45b9-9666-023958d72168","added_by":"auto","created_at":"2024-11-19 16:41:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":216811,"visible":true,"origin":"","legend":"\u003cp\u003eSimulations with both software and hardware as the \"loop\" (Niang et al., 2020).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/cb537c4f83784582e24cf2d5.png"},{"id":69373976,"identity":"dd8d80b8-b27e-440e-b7bc-5fbdbddbcef1","added_by":"auto","created_at":"2024-11-19 16:41:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":457961,"visible":true,"origin":"","legend":"\u003cp\u003eControl panel components.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/def9245523d237c9893af26c.png"},{"id":69374189,"identity":"9dbfc9be-2096-42ca-9c83-d81409dcc538","added_by":"auto","created_at":"2024-11-19 16:49:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":49255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 4: -Sensor Output Voltage vs. Thickness Measurement.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/44f711c4bf8708cd063874e6.png"},{"id":69373974,"identity":"c0789cb9-3807-43f0-867b-98b3bce97fd2","added_by":"auto","created_at":"2024-11-19 16:41:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":52874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 5: -The characteristics Graphs of group measurement 3.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/306361e4a1611480a07c1a1e.png"},{"id":69374190,"identity":"66600494-35ea-4c97-8fc8-a2fd06c14f7a","added_by":"auto","created_at":"2024-11-19 16:50:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2927448,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/a5936a4e-e7d9-42ae-a7c6-288fd42cafa0.pdf"},{"id":69372946,"identity":"1e2a998b-c3fe-40cf-a4f2-facb0d483ef4","added_by":"auto","created_at":"2024-11-19 16:33:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1798686,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4489897/v1/7d7e8d7f021f88eddf44775d.docx"}],"financialInterests":"","formattedTitle":"Improving the Working Efficiency of a Heavy-Duty Metal Cutting Machine Through Closed-Loop Feedback","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe advent of industrial technology has notably advanced the capabilities of metal cutting machines, which are critical in various sectors, including oil and gas production and manufacturing [Qiu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]. Among these, the HACO Hydraulic Guillotine Shear (Type PS 2532) represents a significant advancement, designed for precision in cutting metal sheets of varying thicknesses [HACO (PS 2532), 2008]. However, the potential for human error, as illustrated by a costly data entry mistake leading to significant damage to the machine's hydraulic systems, underscores the need for improved accuracy and efficiency in these machines [Rajkumar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. This incident not only halted production but also highlighted the vulnerability of such sophisticated machinery to simple operational errors [Goeritno \u0026amp; Pratama, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent literature underscores the importance of integrating advanced sensors and control systems into industrial machinery to enhance performance and reduce error rates [Kirjan\u0026oacute;w- Błażej et al., 2023]. Lee et al. (2015) emphasizes the balance between cutting performance and speed, suggesting that technological improvements can enhance material removal rates while reducing cycle times [Ge et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. Similarly, Charniya et al. (2010) demonstrated the effectiveness of combining inductive-capacitive sensors for thickness measurement, offering a precision that significantly surpasses traditional methods [Charniya \u0026amp; Dudul, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e]. These studies indicate a broader industry trend towards the automation of measurement and control processes to improve machine efficiency and reliability [Mu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the work of Kolhatkar \u0026amp; Pandey (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Thaysen et al. showcases the potential of monitoring systems and piezoresistive cantilevers, respectively, in detecting minute operational anomalies and enhancing the sensitivity of measurement systems. Such advancements highlight the evolving landscape of manufacturing technology, where precision and automation play increasingly pivotal roles [Kolhatkar \u0026amp; Pandey, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thaysen et al., 2002]. A new specimen design optimized for dynamic tensile tests enabled oscillation-free force measurements, improving the characterization of H340 steel\u0026rsquo;s strain-rate-dependent plasticity and fracture behaviors under varying stress states (Zeng, C., \u0026amp; Fang, X. (2023).\u003c/p\u003e \u003cp\u003eGe et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Ara\u0026uacute;jo et al. (2017) further contribute to this narrative by showcasing the practical applications of these technologies in reducing machining error and improving the efficiency of shearing machines through servo-pneumatic cylinders and real-time position control [Ge et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mashimo \u0026amp; Oba, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. These studies collectively underscore the critical need for innovation in machine design and operation to address the inherent challenges of metal cutting processes [Zhou et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn response to these challenges and opportunities, this study aims to significantly improve the performance of the HACO Hydraulic Guillotine Shear by integrating a closed-loop thickness gauge sensor into its control system [Ghosh et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. This endeavour is motivated by the hypothesis that such integration can minimize human error, enhance operational efficiency, and ensure higher precision in metal cutting tasks [Zhao et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. By examining a range of measurement methods, sensor technologies, and control logic designs, this research seeks to develop a prototype system capable of accurately measuring the thickness of input sheet metal and automatically adjusting machine operations accordingly.\u003c/p\u003e \u003cp\u003e1.1 The objectives of this project\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCreate a comprehensive technical specification for the system hardware and evaluate various measurement systems.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIdentify the necessary inputs (measurements, user defined variables, etc.) and outputs (signals, actuators, human-machine interface) for the control logic.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDesign and conduct virtual testing of the control logic to ensure its effectiveness.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProcure the necessary hardware and construct a prototype system for empirical testing of the control logic.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEvaluate the prototype's performance and its scalability to full scale machine applications, aiming for significant improvements in accuracy and efficiency.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThrough these objectives, the study seeks to address the pressing need for technological advancements in metal cutting machines, with a particular focus on the HACO Hydraulic Guillotine Shear. By leveraging the latest in sensor technology and automated control systems, this research aims to pave the way for safer, more efficient, and more reliable metal cutting processes, ultimately contributing to the broader field of manufacturing and industrial technology [Salas Avila et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThe literature review delves into the complexities and advancements surrounding the efficiency of metal cutting machines, with a particular focus on sensor integration and control systems. It critically examines the existing body of knowledge, identifying gaps that the current research aims to fill [Alli, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetal cutting machines, essential in manufacturing and maintenance, have evolved significantly. However, the integration of advanced sensors and control systems remains a pivotal area for further exploration. The HACO Hydraulic Guillotine Shear's incident, where a data entry error led to significant damage, underscores the vulnerability of these machines to human error and the critical need for more sophisticated control mechanisms [Jiang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe image depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the shearing machine exhibiting critical hydraulic damage across multiple components, with Circle A and B showing structural failure and leakage, and Circle C revealing extensive hydraulic leakage in the cutting area. The damaging effects suffered by the mistaken insert of the actual metal thickness within the control panel, damages to equipment and hydraulic device damaged. More details can be found in \u003cspan refid=\"Sec43\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e 2.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows three tensile specimen models (a), (b), and (c) illustrating strain distribution at various stages of deformation. Model (a) represents the initial state with minimal strain. Model (b) shows increased strain, particularly near the strain gauge area. Model (c) demonstrates progressive strain and eventual fracture initiation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eShearing traits include burrs, hold down marks, and twist. Shearing makes burrs, just like any other way to cut metal, but if it is done correctly, they can be kept to a minimum which is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExisting research, such as Rajkumar et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)'s work on PLC-based control systems, emphasizes the potential of automation to minimize human error, increase efficiency, and enhance monitoring capabilities. Yet, despite advancements, the integration of sensors that can accurately gauge metal thickness and feed this information into control systems to adjust cutting parameters in real-time is still an area ripe for innovation [Rehman et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe literature reveals a broad spectrum of sensors mechanical, capacitive, inductive, laser, LVDT, magnetic, optical, and ultrasonic each with its own set of advantages and limitations [Magori, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e]. For instance, mechanical sensors offer direct contact measurement but may not be suitable for all materials or thicknesses. Capacitive and inductive sensors provide non- contact measurement options, yet their accuracy and applicability vary widely. Laser and ultrasonic sensors show promise for high precision and versatility but come with challenges related to cost, integration complexity, and environmental susceptibility [Essa et al., 2023].\u003c/p\u003e \u003cp\u003eThis research seeks to bridge the gap by developing a robust, reliable sensor integration and control system for metal cutting machines. It aims to select the optimal sensor that combines\u003c/p\u003e \u003cp\u003eefficiency, accuracy, cost effectiveness, and ease of integration with existing machine control panels. This involves a comprehensive evaluation of sensor types, compatibility with the HACO Hydraulic Guillotine Shear, cost analysis, and practical testing to validate performance in real-world settings [Zhao et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe necessity of this research is further justified by the broader implications of sensor integration in enhancing metal cutting efficiency. By automating thickness measurement and data entry, the proposed system aims to significantly reduce human error, improve cutting accuracy, and extend the lifespan of the machinery. This study not only addresses a specific issue but also contributes to the ongoing discourse on the integration of advanced technologies in traditional manufacturing processes, offering insights that could be applied to a wide range of industrial equipment [Maniar et al., 2021].\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003eThe methodology for integrating an ultrasonic sensor into the HACO Hydraulic Guillotine Shear (Type PS 2532) for thickness measurement is detailed, focusing on sensor selection, calibration, control logic design, and system integration to ensure reproducibility and accuracy [Kelemen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sensor Selection and Calibration\u003c/h2\u003e \u003cp\u003eAn ultrasonic sensor was chosen for its non-contact, non-destructive measuring capabilities, offering long-term efficiency and cost savings [Barsan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e]. The selected sensor operates by emitting acoustic waves and receiving the reflected waves from the material surface, allowing for precise thickness measurements. Calibration involved adjusting the sensor to known thickness values, ensuring its accuracy and reliability across its measuring range. The process was carefully documented to facilitate reproducibility [Zhou et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Control Logic Design\u003c/h2\u003e \u003cp\u003eA Programmable Logic Controller (PLC) was programmed to interpret the sensor's signals and adjust the machine's operations accordingly. The control logic was designed to automate the metal cutting process, making real-time adjustments based on the thickness measurements obtained from the ultrasonic sensor. This involved setting up thresholds for the sensor readings that would trigger specific actions by the machine, ensuring optimal cutting performance for various metal thicknesses as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e [Ghosh et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e: \u003cem\u003e- Simulations with both software and hardware as the \"loop\" (Niang et al., 2020).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 System Integration\u003c/h2\u003e \u003cp\u003eThe integration process involved physically mounting the ultrasonic sensor onto the Guillotine Shear and connecting it to the control system via the PLC. Special attention was given to the sensor's positioning to ensure accurate readings and to avoid interference with the machine's operations. The control logic was then tested and fine-tuned to ensure seamless communication between the sensor, the PLC, and the Guillotine Shear [Gluck et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis methodology underscores the significance of integrating thickness gauge sensors into metal cutting machines for enhanced efficiency and accuracy. Through careful selection and calibration of the ultrasonic sensor, alongside a well-designed control logic and meticulous integration process, the project demonstrates a significant improvement in the Guillotine Shear's performance, highlighting the potential for similar upgrades in industrial machinery and it can be shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e[Pandey et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure shows the contents of control panel and the base for the positioning of the sensor, which is close to the control panel. The contents are as following: -\u003c/p\u003e \u003cp\u003e1-Power supply, 2- PLC device, 3A- switch controller, 3B- Connection cables, 4- HMI screen display,5- Alarm flash, 6- the sensor, 7- Affixed support, 7A- Ground surface, 7B- Sensor holder, 8- Magnetics pieces for field and 9 is conveyor belt.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results/Analysis","content":"\u003cp\u003eThe integration of an ultrasonic sensor into the HACO Hydraulic Guillotine Shear (Type PS 2532) for thickness measurement has demonstrated significant improvements in machine efficiency and accuracy. The results are systematically presented, utilizing tables and figures to elucidate the enhancements achieved through sensor integration.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Measurement Accuracy and Efficiency\u003c/h2\u003e\n \u003cp\u003eThe calibration and testing phase involved collecting a series of measurements across different thicknesses to evaluate the sensor\u0026apos;s accuracy and the system\u0026apos;s overall efficiency. A comparison between the ultrasonic sensor readings and the reference values obtained through traditional measurement methods (vernier caliper\u0026rsquo;s) highlighted the system\u0026apos;s precision.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTable 1: - \u003cstrong\u003eComparative Analysis of Thickness Measurements\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1732033114.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe table illustrates the sensor\u0026apos;s high degree of accuracy, with minimal deviations from the reference measurements. The table reflects the precision of measurements by indicating the meaningful digits in a value, excluding placeholder zeros. They depend on the instrument\u0026apos;s accuracy and uncertainty. In measurements using an ultrasonic sensor and vernier caliper, significant data are determined by the precision of these tools and reported consistently to match their resolution.\u003c/p\u003e\n \u003cp\u003eThe deviations in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e include both positive and negative values, reflecting the fact that the ultrasonic sensor\u0026apos;s readings can sometimes be slightly higher or lower than the reference thickness.\u003c/p\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.1 Positive Variance\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn cases where the deviation is positive (e.g., 1.02 mm compared to 1.0 mm), the ultrasonic sensor may have recorded a value slightly greater than the actual reference thickness. This could occur due to various factors such as surface irregularities, sensor calibration errors, or variations in the material\u0026rsquo;s density.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.2 Negative Variance\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eNegative deviations (e.g., 4.98 mm compared to 5.0 mm) indicate the sensor measured a value lesser than the reference. This is expected in cutting processes since material removal often results in the final thickness being smaller than intended.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.3 Measurement Instrument Error\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIf the ultrasonic sensor or vernier caliper was solely responsible for the deviations, it would likely produce a consistent error, leading to either all positive or all negative deviations. However, the presence of both positive and negative deviations suggests that the errors are a combination of instrument precision limitations and real-world variabilities in the material and measurement environment.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eIn summary, the occurrence of both positive and negative deviations suggests that the ultrasonic sensor is not perfectly calibrated, and the errors are random rather than systematic. This means that sometimes the sensor slightly overestimates the thickness, and other times it slightly underestimates it.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 System Linearity and Sensitivity\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the relationship between voltage (y-axis) and thickness (x-axis). The regression equation y\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.287x\u0026thinsp;+\u0026thinsp;10.043y = -0.287x\u0026thinsp;+\u0026thinsp;10.043y\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.287x\u0026thinsp;+\u0026thinsp;10.043 and R2\u0026thinsp;=\u0026thinsp;0.9999R^2\u0026thinsp;=\u0026thinsp;0.9999R2\u0026thinsp;=\u0026thinsp;0.9999 indicate an extremely high correlation between the measured voltage and the actual thickness, suggesting a very reliable sensor. Key points:\u003c/p\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.1 Slope\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe negative slope (-0.287) indicates that as thickness increases, the voltage decreases consistently. This shows the sensor\u0026rsquo;s response is inversely proportional to the thickness.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.2 High R-Squared Value\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eR2\u0026thinsp;\u0026asymp;\u0026thinsp;1R^2 \\approx. 1R2\u0026thinsp;\u0026asymp;\u0026thinsp;1 suggests that the model can predict thickness based on voltage with minimal error.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Group measurement (3)\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows Multiple Metrics, and this figure breaks down several performance metrics that further explain the sensor\u0026apos;s behavior.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.1 Deviation\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe deviation increases linearly as the thickness increases, which implies that the sensor\u0026rsquo;s accuracy deteriorates slightly at higher thicknesses. This is important because it shows that the sensor\u0026rsquo;s performance changes with thicker materials.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.2 Tolerance\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe tolerance graph is flat, which implies that the tolerance remains constant across different thickness measurements. This suggests that the instrument is consistently within an acceptable error margin throughout its range.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.4 Accuracy\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAccuracy fluctuates across thickness values, with the highest accuracy seen around the 30 mm range. There is a noticeable inaccuracy at the lower thicknesses, which could indicate a limitation of the sensor\u0026rsquo;s resolution or calibration issues at smaller measurements.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.4 Repeatability\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eRepeatability peaks around the 10\u0026ndash;15 mm range but decreases at both ends of the thickness spectrum. This shows that the sensor performs best within a certain range of thickness, but the repeatability is less reliable for very thin or very thick materials.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.5 Linearity\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eLinearity remains close to 1.0, which indicates that the sensor maintains a good linear response overall, with some slight fluctuations. This reinforces the earlier finding from Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e that the sensor is quite reliable in terms of its response over varying thicknesses.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Operational Efficiency\u003c/h2\u003e\n \u003cp\u003eThe integration of the ultrasonic sensor not only enhanced measurement accuracy but also significantly improved operational efficiency. The automated system reduced manual data\u003c/p\u003e\n \u003cp\u003eentry errors and increased the speed of the adjustment process for different material thicknesses.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEfficiency Gains\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMetric\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBefore Integration\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAfter Integration\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage Setup Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(seconds)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eError Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable 2 showcases the efficiency gains post-integration, highlighting reduced setup times, lower error rates, and significant material waste reduction.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe ultrasonic sensor's integration has clearly demonstrated its potential to enhance the HACO Hydraulic Guillotine Shear's performance significantly. The system's accuracy, linearity, and sensitivity improvements are evident, contributing to operational efficiency and reducing material waste.\u003c/p\u003e \u003cp\u003eThe results validate the research hypothesis, indicating that integrating a thickness gauge sensor into the control system significantly improves the efficiency and accuracy of the HACO Hydraulic Guillotine Shear. These findings underscore the importance of adopting advanced measurement technologies in industrial machinery to achieve higher productivity and precision.\u003c/p\u003e \u003cp\u003eOverall, the project successfully encapsulates the benefits of automating thickness measurement, providing a compelling case for the broader application of similar technologies in the manufacturing sector.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.1Ultrasonic Sensor Integration\u003c/h2\u003e \u003cp\u003eThe integration of an ultrasonic gauge for thickness measurement in the HACO Hydraulic Guillotine Shear, as per international and British calibration standards, reveals pivotal insights into the enhancement of industrial machinery's performance through advanced sensing technologies. This study's findings elucidate the significant potential and practical applicability of ultrasonic gauges under varied conditions, notwithstanding the acknowledged limitations pertaining to extreme temperature and pressure environments. Key outcomes and analyses suggest:\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1 Accuracy and Linearity\u003c/h2\u003e \u003cp\u003eThe initial phase of random measurements and subsequent group analyses underscored a high degree of linearity between voltage outputs and thickness measurements, affirming the ultrasonic gauge's precision. Such findings are consistent with existing literature that underscores the efficacy of ultrasonic methods in non-invasive thickness gauging across a range of materials and conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2 Sensitivity and Repeatability\u003c/h2\u003e \u003cp\u003eGroup measurements indicated notable accuracy and consistent sensitivity across different thickness levels, with repeatability rates ranging from 78.38\u0026ndash;111.54%. These results highlight the ultrasonic sensor's reliability, a crucial factor in its industrial application for ensuring consistent manufacturing quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3 Environmental Robustness\u003c/h2\u003e \u003cp\u003eDespite potential concerns, temperature and material properties showed minimal impact on the gauge's performance. This robustness enhances the gauge's applicability across diverse industrial scenarios, aligning with studies emphasizing the importance of sensor adaptability in varying operational contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4 Technological Integration and Analysis\u003c/h2\u003e \u003cp\u003eUtilizing Python for data analysis brought forward the versatility of probabilistic programming in interpreting complex datasets, aligning with current trends towards digitalization in manufacturing and quality control processes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eWhile this study has demonstrated the ultrasonic gauge's viability and effectiveness, several limitations merit attention:\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Surface Irregularities\u003c/h2\u003e \u003cp\u003eThe sensor's performance on non-smooth or uneven surfaces poses challenges, suggesting a need for further research into adaptive sensing techniques or surface preparation methods to enhance measurement accuracy under such conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Technological Evolution\u003c/h2\u003e \u003cp\u003eRapid advancements in sensor technology necessitate ongoing adaptation of machine control systems, underscoring the importance of future- proofing industrial equipment through modular design and software upgradability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.2.3 Cost Considerations\u003c/h2\u003e \u003cp\u003eFinancial constraints limited the exploration of alternative sensors, such as laser variants, highlighting the need for cost-benefit analyses in sensor selection to balance performance improvements with economic viability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e5.2.4 Sensor Selection Uncertainty\u003c/h2\u003e \u003cp\u003eThe vast array of available sensors, each with unique advantages and price points, complicates the selection process. Future studies should aim to develop comprehensive guidelines for sensor selection based on application- specific requirements and total cost of ownership assessments.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Recommendations\u003c/h2\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 Routine Maintenance and Calibration\u003c/h2\u003e \u003cp\u003eEnsuring the ultrasonic sensor's accuracy over time requires regular maintenance and calibration, adhering to manufacturer specifications and guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2 Control System Verification\u003c/h2\u003e \u003cp\u003eEach maintenance session should include verification of the sensor's integration with the machine's control system, ensuring consistency, safety, and regulatory compliance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e5.3.3 Technological Monitoring\u003c/h2\u003e \u003cp\u003eKeeping abreast of sensor technology developments can unveil opportunities to enhance machine functionality and operational efficiency cost- effectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e5.3.4 Tolerance Adjustments\u003c/h2\u003e \u003cp\u003eRefining the shearing machine's tolerance to the control panel's outputs could further optimize performance, reducing waste and improving precision.\u003c/p\u003e \u003cp\u003eThis study not only contributes to the field by affirming the practicality and effectiveness of ultrasonic gauges in industrial applications but also lays the groundwork for future investigations aimed at overcoming current limitations and exploring new possibilities in sensor technology and machine control integration.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Conclusion and Future Work","content":"\u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Conclusion\u003c/h2\u003e \u003cp\u003eThe integration and calibration of an ultrasonic sensor within a laboratory setting underscored its critical role in enhancing metal cutting precision and operational efficiency. By systematically evaluating the sensor's sensitivity, reproducibility, linearity, and repeatability\u003c/p\u003e \u003cp\u003ealongside verifying calibration tolerances through a meticulously designed control logic facilitated by a Programmable Logic Controller (PLC), this study showcased the substantial benefits of automated thickness measurement. This project's comprehensive methodology from selecting the appropriate sensor, through its calibration, to the design and implementation of control logic significantly upgraded the capabilities of a crucial metal cutting machine. It underscored the transformative impact of automation in elevating the precision and efficiency of metalworking processes, highlighting a step forward in operational safety and financial savings due to reduced material wastage and enhanced accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Future Work\u003c/h2\u003e \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e \u003ch2\u003e6.2.1 Advanced Sensor Integration and Real-time Monitoring\u003c/h2\u003e \u003cp\u003eFuture research could explore alternative sensing technologies, such as laser or optical sensors, to optimize metal cutting operations. Additionally, enhancing real-time process monitoring with closed-loop feedback systems could improve cutting accuracy and efficiency by dynamically adjusting cutting parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e \u003ch2\u003e6.2.2 Industrial Application and Material Properties\u003c/h2\u003e \u003cp\u003eExpanding sensor-integrated control systems to industrial environments would validate their practical impact on production efficiency. Research into how varying material properties affect sensor performance could further optimize material selection and cutting strategies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;It was being funded by the author and Huddersfield University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Mohammed Khamis Mohammed Altwiab conceived the study, conducted the experiments, analysed the data, and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author would like to thank the University of Huddersfield for providing the necessary resources and facilities to conduct this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Mohammed Khamis Mohammed Altwiab is a master\u0026apos;s graduate from the University of Huddersfield, specializing in CNC technology and control systems.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlli, K. 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WWW, haco.com/HSL / HSLX / HSLX-HD Hydraulic Guillotine Shears.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eHydraulic Guillotine Shears.\u003c/em\u003e (2018). WWW, haco.com/HSL / HSLX / HSLX-HD Hydraulic Guillotine Shears.\u003c/li\u003e\n\u003cli\u003eJiang, Y., Song, R., \u0026amp; Yuan, M. (2018). Improvement of Ultrasonic Distance Measuring System. \u003cem\u003eITM web of conferences\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e, 2008. https://doi.org/10.1051/itmconf/20181702008\u003c/li\u003e\n\u003cli\u003eKelemen, M., Virgala, I., Kelemenov\u0026aacute;, T., Mikov\u0026aacute;, Ľ., Frankovsk\u0026yacute;, P., Lipt\u0026aacute;k, T., \u0026amp; L\u0026ouml;rinc, M. (2015). Distance measurement via using of ultrasonic sensor. \u003cem\u003eJournal of Automation and Control\u003c/em\u003e,\u003cem\u003e 3\u003c/em\u003e(3), 71-74.\u003c/li\u003e\n\u003cli\u003eKirjan\u0026oacute;w-Błażej, A., Jurdziak, L., Błażej, R., \u0026amp; Rzeszowska, A. (2023). 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Physical.\u003c/em\u003e,\u003cem\u003e 335\u003c/em\u003e, 113347. https://doi.org/10.1016/j.sna.2021.113347\u003c/li\u003e\n\u003cli\u003eMu, Y., Wen, Y., \u0026amp; Li, P. (2021). Improving Sensitivity and Resolution of Planar Magnetic Inductive Sensors by Optimizing Gap Between Magnetic Films. \u003cem\u003eIEEE transactions on magnetics\u003c/em\u003e,\u003cem\u003e 57\u003c/em\u003e(8), 1-8. https://doi.org/10.1109/TMAG.2021.3088226\u003c/li\u003e\n\u003cli\u003eNarayanan, A., Abedini, A., Khameneh, F., \u0026amp; Butcher, C. (2023). An experimental methodology to characterize the uniaxial fracture strain of sheet metals using the conical hole expansion test. \u003cem\u003eJournal of Materials Engineering and Performance\u003c/em\u003e,\u003cem\u003e 32\u003c/em\u003e(10), 4456-4482.\u003c/li\u003e\n\u003cli\u003ePandey, A. V., Karthik, V., Shaik, A. R., Kolhatkar, A., \u0026amp; Divakar, R. (2022). Estimation of UTS from small punch test using an improved method. \u003cem\u003eInternational Journal of Pressure Vessels and Piping\u003c/em\u003e,\u003cem\u003e 200\u003c/em\u003e, 104818.\u003c/li\u003e\n\u003cli\u003eQiu, Y., Jiang, Y., Wang, B., \u0026amp; Huang, Z. (2023). An Analytical Method for 3-D Target Localization Based on a Four-Element Ultrasonic Sensor Array With TOA Measurement. \u003cem\u003eIEEE Sensors Letters\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(5), 1-4.\u003c/li\u003e\n\u003cli\u003eRajkumar, K., Thejaswini, K., \u0026amp; Yuvashri, P. (2021). Automation of Sustainable Industrial Machine using PLC. Journal of Physics: Conference Series,\u003c/li\u003e\n\u003cli\u003eRehman, R. U., Zaman, U. K. u., Aziz, S., Jabbar, H., Shujah, A., Khaleequzzaman, S., Hamza, A., Qamar, U., \u0026amp; Jung, D.-W. (2022). Process Parameter Optimization of Additively Manufactured Parts Using Intelligent Manufacturing. \u003cem\u003eSustainability\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(22), 15475.\u003c/li\u003e\n\u003cli\u003eSalas Avila, J. R., Lu, M., Huang, R., Chen, Z., Zhu, S., \u0026amp; Yin, W. (2020). Accurate measurements of plate thickness with variable lift-off using a combined inductive and capacitive sensor. \u003cem\u003eNDT \u0026amp; E international : independent nondestructive testing and evaluation\u003c/em\u003e,\u003cem\u003e 110\u003c/em\u003e, 102202. https://doi.org/10.1016/j.ndteint.2019.102202\u003c/li\u003e\n\u003cli\u003eWang, J., Tian, Y., Hu, X., Fan, Z., Han, J., \u0026amp; Liu, Y. (2023). Development of grinding intelligent monitoring and big data-driven decision making expert system towards high efficiency and low energy consumption: experimental approach. \u003cem\u003eJournal of Intelligent Manufacturing\u003c/em\u003e, 1-23.\u003c/li\u003e\n\u003cli\u003eZhao, Z., Ding, D., Fu, Y., Xu, J., \u0026amp; Han, J. (2019). A hybrid approach for measurement thickness of complex structural parts using ultrasonic inspection and on-machine probing. \u003cem\u003eInternational journal of advanced manufacturing technology\u003c/em\u003e,\u003cem\u003e 103\u003c/em\u003e(9-12), 4777-4785. https://doi.org/10.1007/s00170-019-04025-1\u003c/li\u003e\n\u003cli\u003eZhou, H., Jia, W., Li, Y., \u0026amp; Ou, M. (2021). Method for estimating canopy thickness using ultrasonic sensor technology. \u003cem\u003eAgriculture (Basel)\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(10), 1011. https://doi.org/10.3390/agriculture11101011\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\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":"international-journal-of-mechanical-and-materials-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijme","sideBox":"Learn more about [International Journal of Mechanical and Materials Engineering](http://ijmme.springeropen.com)","snPcode":"40712","submissionUrl":"https://www.editorialmanager.com/ijme/default2.aspx","title":"International Journal of Mechanical and Materials Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4489897/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4489897/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this project is to improve the functionality and reliability of the HACO Hydraulic Guillotine Shear (Model PS 2532), widely employed in manufacturing and maintenance workshops. The primary issue addressed was the frequent machine malfunctions caused by erroneous data entry, which resulted in damage to the hydraulic components. To resolve this issue, an ultrasonic thickness gauge sensor was integrated into the machine's control system, with the objective of automating and optimizing the cutting process. A comparative analysis was performed on mechanical, capacitive, inductive, laser, and ultrasonic sensors, with the ultrasonic sensor chosen due to its cost-effectiveness, ease of integration, and high accuracy. Laboratory testing was conducted using a sample control panel to evaluate the sensor's calibration, sensitivity, repeatability, linearity, and reproducibility. The sensor achieved an accuracy of \u0026plusmn;\u0026thinsp;0.05 mm with an uncertainty of 0.02 mm. A programmable logic controller (PLC) was utilized to design the control logic, ensuring precise sensor readings and machine operation. The integration of the ultrasonic sensor resulted in a 15% improvement in operational efficiency and a notable reduction in manual errors. This project presents a thorough approach to enhancing the performance of metal cutting machines through sensor integration, offering a clear framework for improving accuracy and productivity in industrial cutting processes.\u003c/p\u003e","manuscriptTitle":"Improving the Working Efficiency of a Heavy-Duty Metal Cutting Machine Through Closed-Loop Feedback","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 16:33:51","doi":"10.21203/rs.3.rs-4489897/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-11-17T05:38:41+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-16T11:15:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-29T17:54:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Mechanical and Materials Engineering","date":"2024-10-25T20:19:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-mechanical-and-materials-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijme","sideBox":"Learn more about [International Journal of Mechanical and Materials Engineering](http://ijmme.springeropen.com)","snPcode":"40712","submissionUrl":"https://www.editorialmanager.com/ijme/default2.aspx","title":"International Journal of Mechanical and Materials Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37ea6421-b36e-44ea-b70b-bd2e52b636d9","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-05T01:54:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 16:33:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4489897","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4489897","identity":"rs-4489897","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}