Prediction of mechanical properties of polypropylene blends with recycled polypropylene using ANN methods

Prediction of mechanical properties of polypropylene blends with recycled polypropylene using ANN methods | 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 Prediction of mechanical properties of polypropylene blends with recycled polypropylene using ANN methods Jittiwat Nithikarnjanatharn, Araya Smuthkochorn, Jetnipat Pimollukanakul, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6615547/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research employed an Artificial Neural Network (ANN) model for predicting the mechanical characteristics of polypropylene (PP) combined with recycled polypropylene (r-PP), including tensile strength (σ T ), Young's modulus (E L ), elongation at break (e), impact resistance (F), and hardness (H shore D ). The experimental findings indicated that the mechanical qualities of polypropylene (PP) combined with recycled polypropylene (r-PP) improved with the increasing proportion of PP. Nonetheless, impact resistance and elongation at break exhibited a tendency to diminish with an increase in the r-PP ratio. The performance in terms of strength and elongation was satisfactory at an approximate ratio of 80% PP and 20% r-PP. The results were compared with the mechanical properties predicted by the ANN, showing that the predicted mechanical properties corresponded closely with the experimental values. This suggests that the model effectively predicts mechanical properties, as the R² values exceeded 80% for all properties, and the MSE and RMSE values were limited. The MAPE values for tensile strength (σ T ), Young's modulus (E L ), and impact resistance (F) were below 10%, signifying that the model demonstrated considerable accuracy. The MAPE values for elongation and hardness were notably elevated, indicating that the model could properly predict the material's mechanical characteristics across several parameters. Neural Network Polypropylene Polypropylene Recycle Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction In a data-driven society, data evaluation and prediction have become indispensable tools employed across several fields such as health, economics, and engineering [1–2]. Consequently, choosing the appropriate forecasting methods for data analysis is crucial for the precision and efficacy of the outcomes [3]. Essential statistical methods, including polynomial regression, are important, particularly when the correlation between variables cannot be described by a linear equation. Augmenting the power order of the independent variable enables the model to more accurately depict the data's curvature [4–5]. Although polynomial regression may represent non-linearity, it remains constrained in flexibility and its capacity to handle high-dimensional data. Artificial neural networks (ANN), a technique in artificial intelligence that emulates the functioning of the human brain, can effectively learn from data and identify complicated correlations, even nonlinear ones. Numerous researchers have employed artificial neural networks (ANN) across numerous sectors, including energy, electrical, and plastics, particularly when confronted with extensive datasets and intricate patterns [6]. The utilization of neural networks for forecasting mechanical characteristics is extensively embraced, facilitating the reduction of repetitive trials and conserving time and resources [7–8]. Numerous studies have been conducted on this subject, including the contributions of S. K. Mishra, A. Brahma, and K. Dutta [7]; E. M. Golafshani and Ali Behnood [8]; Amlashi, A.T. et al. [9]; Ishtiaq, M. et al. [10]; R. Saravanan et al. [11]; D. Manoj and M. Purushothaman [12]; M. H. Islam et al. [13]; and R. S. Diaz, M. V. Carbonell, and J. E. Gutierrez [14]. Plastic is a significant penetration of the 20th century and is common in its presence. Utilization of plastic has recently increased significantly. In recent decades, there has become a significant accumulation of non-biodegradable garbage. Moreover, they are considered among the most hazardous causes of pollution [15–18]. Plastic has been implemented in several applications, including packaging, building, medical equipment, and electronics, owing to its cost-effectiveness, affordability, simplicity of processing, lightweight characteristics, and chemical versatility [19–21]. Polypropylene (PP) is a thermoplastic widely used throughout several sectors due to its excellent properties, including strength, chemical resistance, and economic efficiency. Moreover, polypropylene (PP) exhibits significant versatility and is utilized in packaging, automotive components, and medical gadgets. The protracted breakdown rate of polypropylene (PP) renders plastic recycling a very effective alternative for waste reduction [22–26]. Recycling polypropylene (r-PP) might compromise the material's mechanical qualities, including strength, durability, and flexibility [27–31]. The authors aim to investigate the prediction of mechanical properties of polypropylene blends with recycled polypropylene using ANN methods, thereby enhancing prediction accuracy and minimizing testing time and resources. 2. Principles and Theories 2.1 Artificial neural network (ANN) Artificial neural networks (ANN) are commonly structured with interconnected computational units, k‍nown as neurons, arranged in layers [32–33]. The first layer has input neurons that provide various information parameters to the network, whereas the last layer contains output neurons that present the computation results. Between the input and output layers, there may exist one or more hidden layers, which stay concealed while their input and output data are retained within the network. Augmenting the quantity of hidden layers enables the network to derive elevated-level statistics and enhance predictive precision, particularly with extensive input data [29,34]. Figure 1 shows the general configuration example of an ANN. 2.2 Performance evaluation The assessment of forecasting model accuracy often employs Frequently utilized error measures encompass Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), which may be computed to evaluate the effectiveness of the ANN model in forecasting, as seen in equations (1)–(3) [35–36]. 3. Materials and Methods 3.1 Data collection This study used an artificial neural network to predict the mechanical properties of polypropy‍lene (PP) blends with recycled polypropylene (r-PP). The properties analyzed included tensile strength (σ T ), Young's modulus (E L ), elongation‍ at break (e), impact strength (F), and hardness (H shore D ). In the ratio of polypropy‍lene (PP) to recycled polypropylene (r-PP) gm equal to 0:100, 20:80, 50:50, 80:20, 100:0 by weight, DCP catalyst was added in the ratio of 0.1 phr and VTMS A-171 coupling agent in the ratio of 0, 1, 3, and 5 phr. The obtained mixture was oven-dried at 60°C for 2 hours to remove moisture. The mixing was carried out using a co-rotating twin screw mixer with an L/D ratio of 40 and a screw speed of 150 rpm, resulting in a production rate of 1 kg/h. The mixing temperature varies from the feed zone to the pressure zone, with temperatures ranging from 180–200°C. The material properties were determined using the following standards: Tensile strength (σ T ), elonga‍tion at break (e), and modulus of elasticity (E L ) were measured according to ASTM D 638 Type I, impact strength (F) according to ASTM D 256; and Shore D hardness (H shore D ) according to ASTM 2240. Which the results of the mechanical properties were analyzed for correlation between the variables as illustrated in‍ Fig. 2 . 3.2 Multiple regression analysis Following the assessment of the material's mechanical characteristics, the resultant data will be subjected to analysis via multiple regression, a statistical technique capable of elucidating the nonlinear connection between independent and dependent variables. Consequently, it is employed to forecast the mechanical characteristics of composites, as seen below. where is constants, x is explanatory variables, n is the degree of the polynomial, and e is the error value in the prediction. 3.3 Artificial neural network model Neural networks commonly require training with input parameters and their related outputs, necessita‍ting a substantial dataset for optimal performance. This research involved the creation of a dataset‍ derived from mechanical property test results of polypropylene (pp) blends with recycled polypropylene (r-PP). In modeling using ANN, there are 3 input parameters selected for this modeling consisting of the proportion of each type of plastic and the silane additive that the network has 2 hidden layers, each with 20 neurons, which the output consists of 5 nodes, including tensile strength (σ T ), Young's modulus (E L ), elongation‍ at break (e), impact strength (F), and hardness (H shore D ), For neural network training, numerical data undergoes organization into training and testing sets, a‍dhering to an approximate 70:30 ratio [7–8]. During the training phase, backpropagation, enhanced by a spe‍ed-up algorithm, refines network errors and parameters through the Rectified Linear Unit function. The procedure of the proposed ANN model is summarized in Fig. 3. 3.4 Performance evaluation When assessing neural networks for predicting mechanical qualities, suitable metrics are necessary to effectively represent the model's performance and dependability. These metrics must account for the attributes of the material data and include Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). given by equations (1)–(3). 4. Results and discussion 4.1 Results of Mechanical properties test The mechanical property assessments were performed in accordance with ASTM standards. The mechanical property test findings of PP and r-PP indicate that an increase in the quantity of PP correlates with an enhancement in tensile strength (σ T ), Young's modulus (E L ), and hardness (H shore D ). The impact resistance diminisheswith an increased fraction of r-PP, attributable to the deterioration of the polymer structure during the recycling process. Materials composed of 100% r-PP have significantly elevated elongation at breaks (e) and superior impact strength (F) compared to pure PP plastic, signifying enhanced durability and impact resistance. Although the mechanical strength decreases, the use of silane helps increase the Young's modulus (E L ) and hardness (H shore D ) [37–39]. Especially as the percentage of PP increases. However, when the concentration of silane reaches 5 phr, the elongation and impact values decrease significantly because the high amount of silane makes the material too rigid, reducing its flexibility. Additionally, a blend consisting of 80% PP and 20% r-PP provides an optimal balance between strength and elongation, as seen in Fig. 3. 4.2 Results of Multiple regression analysis To investigate the influence of the two factors under investigation, the predicted mechanical characteristics of polypropylene (PP) blended with recycled polypropylene (r-PP) were analyzed utilizing a polynomial regression model, with the coefficient of determination (R²) employed to explain data variance. Table 1 demonstrates that the polynomial regression equations have R² values over 90% for tensile strength and Young's modulus, signifying that the model proficiently accounts for variation and can be used for prediction. On the other hand, the elongation at break and impact strength have R² values below 60%, suggesting that the model can adequately account for the data variability. The hardness exhibits an R² value over 60%, signifying that this model was appropriate for prediction with intermediate to high precision. . Table 1 Polynomial Regression Formula Mechanical properties Regression equation R 2 Tensile Strength 20.419 + 0.17246A − 0.1797C − 0.000731A 2 − 0.0141C 2 99.57 Young's Modulus 317.4 + 6.189A + 8.7C − 0.03486A 2 - 2.04C 2 93.95 Elongation at Break 112.1–2.366A - 33.0C + 0.01833A 2 + 5.10C 2 51.88 Impact strength 5.603–0.1093A - 1.010C + 0.000834A 2 + 0.1565C 2 59.28 Hardness 57.365 + 0.1544A + 1.115C - 0.000888A 2 - 0.1190C 2 87.46 Note : (A) was polypropylene (PP), B was recycled polypropylene (r-PP), and C was Silence substance Table 1 showed that the formulated equations may effectively predict some characteristics; nevertheless, the omission of other variables may result in interpretative inaccuracies. This has resulted in the application of artificial neural networks to predict the mechanical characteristics of polypropylene (PP) blended with recycled polypropylene (r-PP). 4.3 Results of Prediction by ANN Model Predicting mechanical qualities in neural networks will involve an input layer, many hidden layers, and an output layer. The Adaptive Moment Estimation (Adam) technique was employed to update the weights in the neural network during training, and it was processed through the Rectified Linear Unit (ReLU) to enable the model to learn non-linear correlations, as shown in Fig. 4. The predictive capability of the neural network model was evaluated by providing the correlation coefficient (R²) for each characteristic as follows: The hardness has an R² value of 94.91%, indicating that the model can accurately predict the hardness of the material. The elongation at the breaking point has an R² value of 81.1%, indicating an acceptable data distribution. The value of the Young's modulus shows an R² of 94.1%, indicating good predictive capability, despite some data dispersion. The impact strength has an R² value of 94.3%, indicating a fairly accurate prediction of impact strength, while the tensile strength shows an R² value of 99.5% [40–41], as shown in Fig. 5. From Fig. 4, the prediction results were compared with the experimental results of the mechanical properties of polypropylene and recycled polypropylene as shown in Fig. 6. It was found that the values of tensile strength (σ T ), Young's modulus (E L ), elongation‍ at break (e), impact strength (F), and hardness (H shore D ), obtained from the prediction, tend to be close to the values obtained from the experiment, indicating that the prediction of mechanical properties using the artificial neural network was very reliable. To elucidate the effects of the examined factors more effectively, a contour map has been generated. This diagram only presents the attributes derived from mechanical property assessments, including tensile strength (σ T ), Young's modulus (E L ), elongation at break (e), impact strength (F), and hardness (H shore D ). Figure 6 shows that as the amount of PP increases, the values for Tensile Strength (σ T ), Young's Modulus (E L ), and Hardness (H shore D ) go up significantly, especially when PP is 50% or more. This can be elucidated by the observation that the molecular structure of pure polypropylene (PP) demonstrated greater hardness and strength compared to recycled polypropylene (r-PP). Nevertheless, increased PP markedly reduces the Elongation at Break (e) and Impact Strength (F) values, indicating a deterioration in the material's toughness balance, resulting in increased brittleness despite enhanced durability [24–26]. Adding Silane in the right amount, especially between 1–3 phr, slightly improves the values of Young's Modulus (E L ) and Hardness (H shore D ). Silane functions as a chemical intermediary between phases. Adding Silane significantly reduces Elongation at Break (e) and Impact Strength (F), especially in the high R-pp group, making the material too stiff and weakening its toughness. The research indicated that increasing the proportion of PP and including silane improves specific mechanical qualities, although it adversely affects the material's toughness. 4.4 Results of Neural network performance validation Table 2 presented the accuracy statistics of the neural network model in predicting the mechanical properties of a composite material composed of Polypropylene (PP) blended with Recycled Polypropylene (r-PP), which includes mechanical properties such as tensile strength (σ T ), Young's modulus (E L ), elongation at break (e), impact strength (F), and hardness (H shore D ). The model will be assessed using MSE, RMSE, and MAPE, which quantify the discrepancy between anticipated and actual values. The MSE and RMSE values were determined to be low in predicting tensile strength (σ T ), impact strength (F), and hardness (H shore D ). This finding suggests that the model possesses significant promise for predicting stable mechanical characteristics [8]. The elevated RMSE value for Elongation at Break (e) indicates that the model may not completely comprehend the intricacies of this characteristic [41–42]. The ANN model works very well, accurately predicting the mechanical properties of the PP:r-PP composite, as shown by overall MAPE values less than 10% for different parameters. Nonetheless [43–44], the Elongation at Break (e) value exhibits significant data variability, resulting in diminished accuracy of the model's predictions relative to other attributes. Table 2 The performance of the ANN neural network Material Performance Tensile strength Young's modulus Elongation‍ at break Impact strength Hardness PP : r-PP MSE 1.10 813.99 853.12 0.75 0.24 RMSE 1.04 28.53 29.21 0.87 0.49 MAPE 3.16 5.32 16.18 0.96 10.83 5. Conclusions This research employed an Artificial Neural Network (ANN) model to predict the mechanical characteristics of polypropylene (PP) combined with recycled polypropylene (r-PP), including tensile strength (σ T ), Young's modulus (E L ), elongation at break (e), impact resistance (F), and hardness (H shore D ). The experimental findings indicated that the mechanical qualities of polypropylene blended with recycled polypropylene improved with the increasing proportion of polypropylene. Nonetheless, impact resistance and elongation at break showed a tendency to decrease with an increase in the r-PP ratio. The performance in terms of strength and elongation was satisfactory at a mix ratio of 80% PP and 20% r-PP. The results were juxtaposed with the mechanical properties predicted by the ANN, revealing that the predicted mechanical properties closely aligned with the experimental values. This indicates that the model effectively predicts mechanical properties, as the R² values exceeded 80% for all properties, and the MSE and RMSE values were limited. The MAPE values for tensile strength (σ T ), Young's modulus (E L ), and impact resistance (F) were below 10%, signifying a high degree of accuracy in the model. The MAPE values for elongation and hardness were significantly elevated, indicating that the model could properly predict the material's mechanical characteristics across several parameters. Declarations Acknowledgment The author would like to thank all lecturers in the Industrial Engineering Program, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, for supporting this research. References F. Tavazza, B. Decost, and K. Choudhary, 2021. 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Model Dev , Vol. 15(14) : 5481 – 5487. E. Guresen, G. Kayakutlu, and T. U. Daim, 2011. Using artificial neural network models in stock market index prediction, Expert Systems with Applications , Vol. 38(8) : 10389 – 10397. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6615547","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456500923,"identity":"549895d6-2f9f-4248-9ee3-2ab3921f7ade","order_by":0,"name":"Jittiwat Nithikarnjanatharn","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACxmYgkQBhNj4AUWwQiQN4tDAzNgC1SICYBkABCYiWBNxaGBiAWhjAWhjYJKAM/FqY2/mPP3jYZlfHIN3cVl2Zc7iOj4H54QfGH3fwOyyxLVmCQeZg282z2w4DHcZmLMGQ8IyAlm3MEgwSiW03G8FaGMyADjtMSEs9WEshRAv7N2K0HAZrYYRo4SFoi+GMxH/HJdskEpslG7elS7Yx8xRLJKTh1mLYf/DBxx9nqvn5JdIffmzcZs0v396+8cMHGzxaGqAMNrgQMwMsPWAH8njkRsEoGAWjYBRAAADTQk1KHiQZgAAAAABJRU5ErkJggg==","orcid":"","institution":"Rajamangala University of Technology Isan","correspondingAuthor":true,"prefix":"","firstName":"Jittiwat","middleName":"","lastName":"Nithikarnjanatharn","suffix":""},{"id":456500924,"identity":"05a1122c-839a-497d-a495-87957014f7a7","order_by":1,"name":"Araya Smuthkochorn","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Araya","middleName":"","lastName":"Smuthkochorn","suffix":""},{"id":456500925,"identity":"0eff8b6f-d7e4-42f9-aeb0-d590d49a44b8","order_by":2,"name":"Jetnipat Pimollukanakul","email":"","orcid":"","institution":"Rajamangala University of Technology Isan","correspondingAuthor":false,"prefix":"","firstName":"Jetnipat","middleName":"","lastName":"Pimollukanakul","suffix":""},{"id":456500926,"identity":"233c2973-9b10-49e5-8b1e-17775606810b","order_by":3,"name":"Wannisa Nutkhum","email":"","orcid":"","institution":"Rajamangala University of Technology Isan","correspondingAuthor":false,"prefix":"","firstName":"Wannisa","middleName":"","lastName":"Nutkhum","suffix":""},{"id":456500927,"identity":"a68e8203-c6a5-41c0-85d3-633c95c22891","order_by":4,"name":"Ukrit Thanasuptawee","email":"","orcid":"","institution":"Rajamangala University of Technology Lanna Tak","correspondingAuthor":false,"prefix":"","firstName":"Ukrit","middleName":"","lastName":"Thanasuptawee","suffix":""},{"id":456500928,"identity":"1072d528-67ee-4df3-99a5-05fea74bffa3","order_by":5,"name":"Kitti Wirotrattanaphaphisan","email":"","orcid":"","institution":"Rajamangala University of Technology Lanna Tak","correspondingAuthor":false,"prefix":"","firstName":"Kitti","middleName":"","lastName":"Wirotrattanaphaphisan","suffix":""},{"id":456500929,"identity":"5206a594-6656-4428-908c-1066ce68d3c7","order_by":6,"name":"Panuput Satuwong","email":"","orcid":"","institution":"Rajamangala University of Technology Isan","correspondingAuthor":false,"prefix":"","firstName":"Panuput","middleName":"","lastName":"Satuwong","suffix":""}],"badges":[],"createdAt":"2025-05-08 01:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6615547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6615547/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82902709,"identity":"304f82d4-7384-4348-badb-55d0592cf78f","added_by":"auto","created_at":"2025-05-16 13:42:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38945,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral configuration of ANN [29]\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/9618826686e5d9a2bcc3c983.png"},{"id":82901673,"identity":"10a7e2f5-7ff5-40ba-80c6-a6ab8be931a0","added_by":"auto","created_at":"2025-05-16 13:34:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":761563,"visible":true,"origin":"","legend":"\u003cp\u003eMechanical testing equipment (a)\u003cstrong\u003e \u003c/strong\u003ehot air oven, (b) twin screw extruder (c) universal testing Machine, (d) impact test machine, and (e) Hardness Tester\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/443cecc52c69f6b49541b7fa.png"},{"id":82901671,"identity":"c5621f42-9ec5-486c-af61-54d4f52139c6","added_by":"auto","created_at":"2025-05-16 13:34:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32272,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2. The proposed ANN algorithm.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/633421cc08ef4633884e52b1.png"},{"id":82903411,"identity":"82212415-4d0a-4136-99f5-8277df26b32e","added_by":"auto","created_at":"2025-05-16 13:50:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":321033,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3. Parameter of Mechanical properties\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/41aee5880f13afef3dee88ed.png"},{"id":82901675,"identity":"e74bbd12-58b0-47be-95e2-718f9cc759aa","added_by":"auto","created_at":"2025-05-16 13:34:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25720,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4. Neural network model (ANN)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/ed4e4d124be7ebc26a97acdf.png"},{"id":82902714,"identity":"dd0c3055-fc02-4d7d-845e-a51edf307ff8","added_by":"auto","created_at":"2025-05-16 13:42:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":324383,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5. Forecasting Mechanical Attributes of PP When combined with r-PP.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/06a0ba7ad943e3cf04695526.png"},{"id":82902715,"identity":"6e1a1957-b76f-4860-9e5c-16055cf42f00","added_by":"auto","created_at":"2025-05-16 13:42:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":400171,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6. A comparison of artificial neural network (ANN) predictions with experimental results for mechanical‍ properties.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/557905fa3ccae9c35feb876a.png"},{"id":82901684,"identity":"e04191cd-783f-458c-bd69-4e6610f75667","added_by":"auto","created_at":"2025-05-16 13:34:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":359197,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7. The correlation between PP:r-RR composite plastic per the quantity of consisted of reactive coupling agent affecting mechanical characteristics\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/34db42d26975d19ff9816389.png"},{"id":85285115,"identity":"fa383806-abfd-4a90-813d-32825a69dcb7","added_by":"auto","created_at":"2025-06-24 08:54:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2948526,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6615547/v1/26dfe703-5d9b-4a6e-af4f-8f0494d8866f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of mechanical properties of polypropylene blends with recycled polypropylene using ANN methods","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn a data-driven society, data evaluation and prediction have become indispensable tools employed across several fields such as health, economics, and engineering [1\u0026ndash;2]. Consequently, choosing the appropriate forecasting methods for data analysis is crucial for the precision and efficacy of the outcomes [3].\u003c/p\u003e \u003cp\u003eEssential statistical methods, including polynomial regression, are important, particularly when the correlation between variables cannot be described by a linear equation. Augmenting the power order of the independent variable enables the model to more accurately depict the data's curvature [4\u0026ndash;5]. Although polynomial regression may represent non-linearity, it remains constrained in flexibility and its capacity to handle high-dimensional data. Artificial neural networks (ANN), a technique in artificial intelligence that emulates the functioning of the human brain, can effectively learn from data and identify complicated correlations, even nonlinear ones. Numerous researchers have employed artificial neural networks (ANN) across numerous sectors, including energy, electrical, and plastics, particularly when confronted with extensive datasets and intricate patterns [6]. The utilization of neural networks for forecasting mechanical characteristics is extensively embraced, facilitating the reduction of repetitive trials and conserving time and resources [7\u0026ndash;8]. Numerous studies have been conducted on this subject, including the contributions of S. K. Mishra, A. Brahma, and K. Dutta [7]; E. M. Golafshani and Ali Behnood [8]; Amlashi, A.T. et al. [9]; Ishtiaq, M. et al. [10]; R. Saravanan et al. [11]; D. Manoj and M. Purushothaman [12]; M. H. Islam et al. [13]; and R. S. Diaz, M. V. Carbonell, and J. E. Gutierrez [14].\u003c/p\u003e \u003cp\u003ePlastic is a significant penetration of the 20th century and is common in its presence. Utilization of plastic has recently increased significantly. In recent decades, there has become a significant accumulation of non-biodegradable garbage. Moreover, they are considered among the most hazardous causes of pollution [15\u0026ndash;18]. Plastic has been implemented in several applications, including packaging, building, medical equipment, and electronics, owing to its cost-effectiveness, affordability, simplicity of processing, lightweight characteristics, and chemical versatility [19\u0026ndash;21]. Polypropylene (PP) is a thermoplastic widely used throughout several sectors due to its excellent properties, including strength, chemical resistance, and economic efficiency. Moreover, polypropylene (PP) exhibits significant versatility and is utilized in packaging, automotive components, and medical gadgets. The protracted breakdown rate of polypropylene (PP) renders plastic recycling a very effective alternative for waste reduction [22\u0026ndash;26]. Recycling polypropylene (r-PP) might compromise the material's mechanical qualities, including strength, durability, and flexibility [27\u0026ndash;31]. The authors aim to investigate the prediction of mechanical properties of polypropylene blends with recycled polypropylene using ANN methods, thereby enhancing prediction accuracy and minimizing testing time and resources.\u003c/p\u003e"},{"header":"2. Principles and Theories","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Artificial neural network (ANN)\u003c/h2\u003e\n \u003cp\u003eArtificial neural networks (ANN) are commonly structured with interconnected computational units, k\u0026zwj;nown as neurons, arranged in layers [32\u0026ndash;33]. The first layer has input neurons that provide various information parameters to the network, whereas the last layer contains output neurons that present the computation results. Between the input and output layers, there may exist one or more hidden layers, which stay concealed while their input and output data are retained within the network. Augmenting the quantity of hidden layers enables the network to derive elevated-level statistics and enhance predictive precision, particularly with extensive input data [29,34]. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the general configuration example of an ANN.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Performance evaluation\u003c/h2\u003e\n \u003cp\u003eThe assessment of forecasting model accuracy often employs Frequently utilized error measures encompass Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), which may be computed to evaluate the effectiveness of the ANN model in forecasting, as seen in equations (1)\u0026ndash;(3) [35\u0026ndash;36].\u003c/p\u003e\n \u003cp\u003e\u003cimg 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e/fuoYLafmVCUEet8iSQb2pbKY9RAjNq1KnBzssp+ocNuin/QWr681rjXncjQls74Gin3g/rJo/R3jxHWsva8i7s//xLTlvXr9ZbkjRdzjvvvPT/0KFD6f+oiDdOOeWUZmh4BsYzIGqNufXeqzkCgQcHRF6LSy0fwSFo7pAHJG0B4yi4lZ4HgmyTAC/eVhDBVlm73CYeYGMdNIEgPwRMke5oUsAw0/rpVybUkLKdSD/TmJ9xfJEYFstTHqMEZvHAYt7Eglp3gtxhm05QOzvo/o1a466mIDn2a1kuHJ+D3n3gzgf7rjyOSes4TyJLkmYb1wUqasoH2YfBNRhjVY4c0dSgsTi7JLo62GimfPUQVx28pP5ooJ53+QNQ+XiWC+VyrD/6v/vd7x41jW3lw/l6cpHGfF105cNh8XBd2UWekOef7bHO+kPSTD16G4xnnuiny6eVYhpdmZd8vUxj+chXqa3syy6W7UpTPm9buebT83Tk6aTLy65NOU+5POUd8vSVxyEd+4+O/Jf7slca27r8WEWsT5K0vHF9aLsuljEJHdeOEteg/Jo0ihX8qTcgLShqaKlZrQ/2nrXgGh1lTNOH+WhPPUnUQFNbbPMHSdKo1wTuRq5ZsyY1pxuHTSmkJYqTCk0qBm27uxhIG2k0KJYkgcocmui1ve2oC00oCIqpbBuXgbG0hNHGmlrjQdp3LzRqBUhbtEGXJAk8E8VD2dFmuBdqiqlhpqZ4EnegbUqhBZf/ghv2798/kffySpIkjcPAWJIkSarZlEKSJEmqGRhL0oz69NNP0wMn/Nrfzp07qy+//LJ66KGHqrVr1879CiD9jGOaJKk3A2NJWgIIknnDx7Zt26oDBw40Y6vUz7irr77a4FiS+jAwlqQlgBpjns7+5S9/WR0+fDg9oU1QvGHDhjR979691WOPPZb6JUntDIwlaYng9Xc33XRTtXLlyjR8xhlnVM8///xccPziiy9aayxJPRgYS9ISQPC7bt26ZuhrBMknnXRS6v/ggw9SbbIkqZ2BsSQtAQS/UVNcOuuss5o+SVIvBsaStAQY/ErS+AyMJUmSpJqBsSRJklQzMJYkSZJqBsaSJElSzcBYkiRJqhkYS5IkSTUDY0mSJKlmYCxJkiTVVhypNf2SJEnSsmWNsSRJklQzMJYkSZJqBsaSJElSzcBYkiRJqhkYS5IkSTUDY0mSJKlmYCxJkiTVDIwlSZKkmoGxJEmSVDMwliRJkmoGxpIkSVLNwFiSJEmqGRhLkiRJNQNjSZIkqWZgLEmSJNUMjCVJkqSagbEkSZJUMzCWJEmSagbGkiRJUs3AWJIkSaoZGEuSJEk1A2NJkiSpZmAsSZIk1QyMJUmSpJqBsSRJklQzMJYkSZJqBsaSJElSzcBYkiRJqhkYS5IkSbUVR2pNvyRpQCtWrGj6NGu87EnqYmAsSSOIwNhTqCQtHTalkCRJkmoGxpIkSVLNwFiSJEmqGRhLkiRJNQNjSRqRD95J0tJiYCxJ8+jTTz+t1qxZU/3qV79K/3mbBd0LL7zQzCFJmhYGxpK0AO68887queeeS7XMO3bsqG688cbq7bffbqZKkqaBgbEkLYC77rqr+ta3vpX6169fn/5/8skn6b8kaToYGEvSAli7dm3TV1UnnHBCddxxxzVDkqRpYWAsSZIk1QyMJUmSpJqBsSQNibdKSJKWHgNjSdLM+/LLL6uLL7547nV4dDt37mymStJgDIwlSTONoPjqq6+u3nnnnepPf/rT3Cvxtm/fXm3evLmZS5L6W1GfQPzpJkkaQjSl8PQ5HXgf9D/90z9Vu3btqq688spmbJVqjB955JHq9ddfr0488cRmrCR1s8ZYkrQgCGCPP/749MWC/5P6gRPeD/23v/3tqKAYvCLviy++qD777LNmjCT1ZmAsSSOwtng4/DT2xo0bq88//zw1ceA/w4yfL88++2x6XzTvjZakQRgYS9KMIZhcs2ZNetiM9rWzgJ/Efv/996sNGzZU99xzT7Vp06Y0zPhBRG3zoG2GX3jhherJJ5+stmzZYjMKSQMzMJakCSFIvfXWW1NTAbqbb755XgLXCDK3bt1arVy5shk7vSJIRaSZ/6tWrUrjB3l7BM0lrrjiijQ/6+uF6VdddVUKvtkfkjQoH76TpCER9LadOqM28xe/+EX6T8C3f//+6vnnn59YABtBHzWvk1zvLKCm/IILLkj9XQ/URfnwVgqDYknDssZYkiaAoO3QoUPVfffdl4JVOmqM//73v1fvvvtuM9d4qH1++OGHU/+s1BZPEoEwTSOoLX/66aebsV/ji4hBsaRxGBhL0ozYt29ftXfv3lRbvG7dumbs8rJ+/frUBIPXsOUP7hEU81Dfnj17DIoljczAWJImgNrM1atXp/a/1OzSPf7449U//MM/VGeccUYz1+jy2uLLL7881RbTbCDaM3e1u2W5+EW4QdryTtqk0xhtjfNaYx7M++lPf5pqistXtknSMAyMJWkIBG9daFvMmxO+8Y1vpO6TTz6ZWDtgmmPwy27UllJrinPOOac67bTTUj+vJmvTttxCmo80XnPNNel/1Bq/+uqrc6+BiyA8ukm+L1nS0mdgLEkTQgBMjScP5tFRYzypdsAR/J199tlzNdDUUucPo+VNC0LbcgtpPtJ40kknpSCaWuO33norNZ2IMi87fviDWmZJGoSBsSRNOZoavPjii6mfoDAPtqP2tO0X3vLlovnFYph0GgmeCaLxl7/8Jf2XpEkwMJakCaA5AG+hiJ88jo5g77e//W0z12gOHz5cffDBB6k/gswQTRWocaXmNbfYzSjCpNNI8MwXBPz5z39O/yVpEgyMJWlMzzzzTPWd73wnvTHiD3/4Q7qFTzC7bdu26qWXXkoPi43z4Bu1rNS2tsmbKlDzSg1sWOxmFGE+0njWWWel/7TjztcnSeMwMJakIRH4Bh7s+uEPf5iCO96xG8EdtZo8DBYPnpWvFxsGwR/rp1Y1akpzUYtMzSs1sJiWZhRhvtJITTpfQiRpEgyMJWkMEbS2yWtK51NbU4VpaUYRZiGNkmRgLEljIOA799xz+wZ3bQ+eTUpbU4VRmyhQA162k45uzZo1I9d6TzKNkjRfDIwlaQwEfG+++eYxrwUjwKR5wJNPPtmMmV9lU4V4KG0amlGEWUijpOXNwFiSJoRa0IceeijVuP7oRz+qfvKTn1SbNm1qpo4u3ttL7SpNN9rkTRV4ty/vDMbatWvTf1DbS61v1y/QgeCeID9/F3B0Bw8eTF8ERtUvjYOkL0RQ/c1vfjP9mIokTYKBsSRNAG+mOPXUU6vdu3enN1PQXGBSPyxxwgknVMcdd1zq73pvb95U4cEHH0xNNwhCCUYD8xDcLtbPJvdL4yjpK9/rLEnjMDCWpDFQS7x58+bq+uuvT69lo1nFpNvLUiNKzSh6vbc3miocOHAg1coShI5TwzsfJpFGapajtjle2yZJk2BgLEkD4gG00mOPPZbaEVPzed999zVjJ4saUdrhotd7e6OpQoggNAzTVGG+9ErjoOmL9zr7NgtJk2ZgLEkjIkCN9/DOd1tXAkACwfw9wKW8qULZjGJaTCKNvs1C0nwxMJakEeU/1dz2QxMEzl0Pyw2LAJBAkIAw3gPcJpoWTGMzijBOGvMvI77NQtKkGRhL0ojytr/vv/9+9b3vfW+uNpeH8S699NLqr3/9axomoCVIZjpvrhgWAeDWrVtTf7wHuJQHjWUzimkxbhopP2rNqW2+4YYbmrGSNBkGxpI0BF5bFvJgFXv37q3OPPPMirbIP/vZz6pdu3ZV3/72t5upVXXVVVdV3/3ud6vrrruuGTOcdevWVRs2bEjb2bdvXzP2axE0Mg/zTqNx0/jwww+nLxn8/Pa01ohLml0GxpI0Bl4t9pvf/CYFxKAd8I9//OPU3IHmDwTO/DIetm3blsaPGtDlgTgBYl5rTMB52223paBxWpsYjJvGeBuFtcWS5suKI3n1hySpEzXB03DK5PVwvAljz5496b3G27dvb6ZUqSb2+eefbw06CSxp1/vzn/98Qd9lvHPnzoHS2C99eb4X613MkpY2a4wlacbwWjhqTak15gcuwrXXXls99dRTU1dbnP/63qhp5Ce2qZnnlwQNiiXNF2uMJWlA01JjPKrFqjEe1LSnT9LSZ2AsSQOa9cBYktSbTSkkaUKo8Rz1l+VoKnDeeeeldUiSFoc1xpI0oPmqMY4mBOCtC76GTJIWhzXGkjQAguL5QO3y6tWr0w+ESJIWl4GxJE3IsE0pmI8f/dixY0d6BZkkaXEZGEvSIuHNCzTNuPXWW5sxkqTFZGAsSfOAH6Og+UVXx3RJ0nQxMJakefDEE0+k2uCujumSpOliYCxJAyKglSQtXQbGkjQPbEohSbPHwFiS5oFNKSRp9hgYS1OO99uefvrpzdDsopZ0Wk1z2iRpObrsssvSDx71M+lr5JINjCnQ/LZl3jFtPm3ZsqXpOxbT8rT0wgGRzzvIATJfHnjggbl0xAHYK5/TqizTtm6a8vXMM8+k9+K+9957R+2Dtq7Efopp/Y75ct2hLC9OQMwbP0bR63MWXcx78ODBNLyYx3Eb0iRJmh6cl2+88cajfhE0ril0XIfCaaedVu3atSuNj+vNWI4sYfWFmCdljuzevbsZcyT1M64ONpoxk8U2B1k3aSjTVrr00kvTPLfcckszZnHcf//9KR3kDVGu81WGC4Ey7Uo/5T4NopxLpDs/JmK+fB+F/fv3d07LxTz58RjL5uPYdrmu2H7bscyxw3pCzNsrLQuJtETXzyDzSJLGwzU4v55wvcivy0zjfMz1JRfXrHEtu6YU1113XVUHFan2aj5qrgatbawDjNTdfffdzZijRdqYZ7G9+uqrqcz4Vgb+U35L1UsvvdT0LS6OpfoE0Ax1Y3/EfM8991z6n6tPKOk/v67Whm/e7N/SG2+8kY4/PjOBmuthjsnbb7997hs/SGt9MpvJuw2SpPnFXVLk150333zzqOsy07iuEZvkuNZwLRv3+rIs2xhHVfvq1avT/1x++7mtzUqv6nzmf/nll+duGffbOffee2+aNw6E3NNPP90ZNCNPQxngl7e381sLpCnyFdMHaVry6KOPHrUdApxLLrmkGfpavm3yFevOb9VHeqKsy+3HfHTlPmCYPLBupuflny83ypce0pWvD5Fu8D/SQ5rp4njI93VeBrFsiPn5H/O13fphOsdSfnLo5aOPPkr/TznllPQ/x77iZME+bNvW448/Xm3fvr0ZOhrHZ1kmN998c9PXW9fxv3HjxpS3trQspNg3R1Jl8NfDbXpNkyRNBnEPTShybddBrmt0pRtuuCFd68byVcXx0lRf1FO1el4lXwcIaRz/S2vWrDmqap7hvIiimp715sP5uuhnuX5iHv6X87P+fHq+/shTpKFs5lBuP08f/xlmOv8Rechvd5dinli2C7c68rQyHNtB3P6ItIJ56EKe39hu7L9Id2yH/+S/X5m0YfkyL2wn3/+xntgm01mG/hgXaYk0x3CIeUlLXo6MbyuPwLa7yjovI8R62uZnm8wbZZQvB5bNpzOcY1x0XeXZtWxX+sG0vKyHEWXaqyvTUor5Qjlc6poe4+1mo5M0veJa0iseCVxDuuZjHf2uAb0s6TNFFHLecVFtQyEyPReBTFzAy6AHDDNPBA0M9woIQswT2813IuuIYebLt0la8oAi8hjjmDfPI/35cKQ3V26/C/NFV+axrfxIUz4u5omyQpk+1pvnj/nzYaaX22Z6Pk9ZJm2iHMquXIZhxpcfQNLM+FwcL3n+Ii2xD8tjqktZLjnyzzryrgvbi21HnvP0sS6GI51tx0HkNbp8ecSybV0X1hnpmgb90ttvuiRpPG0xQpv8utamjCOGtSyaUtSFzRUttUnhFm6b11577Zi2k7RXYdyHH36Yhln21FNPTf2BanscOnQo/R8WtwjYRjSb4PbyK6+80nkLnTY1d9xxR7q1SxdpjjQ+8sgjqS0O62F6W37LfCJuxfdCGdYHbOrnf357mfKjfMdFG1bapUZTiTZlE45+ZdKF+chTdBwnXfJ2sqHML21ykd/eoZ/5ymYD559/ftM3mvqkkNJcf/jT8CBNR6K5RLQ1powpy7bbUTmOJ7YV5cr/tu3F5yy6tuNMkqRx0AyVWGe+LKs2xtF4u2zTutjytsY8PNWvDSfBUB6A0MVBEgExAQ/jJxGs5m1FCaJYbwSRZfvTcUVATKDNdgbVq0wGdd555zV9s4MvEezjCy+8sBnTjX1HQB1tjXm9TXyxa8O+zQN6vrTEFyOO2X7a2qBPQtmGu63jOJIkLS2c/+czKMaye/hu//79qRa1DOioCeaiX9aEMe6iiy5K/dSA8aBS7uOPP07/22oUB5XXGlPzSbDTheCmfBITEbwSjBAkEsRMCsFRWS6R5lxXbXw4+eSTm752bOf6669P+2iYA79fmQyK9VD25HWUgD8efCuDMvbF+vXrm6HBMP+g+3CYL3xRaxxBa7/jtnzLRQTXg4h92JYu8lbefRlU1GD36rruuHRhGRBUS5IWXlROdd2B55pOnMR1qBfitrYH0Qe17AJjAgECRwLQPHAiICLQy2veCI4YFxdZatgo8Hw5dlLczkYE2BgmMIta43xdbQhsysCeQCyCd9aRNyHoF6wOinLJg2O2ybZ4wwAi4Bokz7x6BayD9NERPMWHIb5stN2ub9OvTIZBcE5eI1/D4Dih9pbgPmpao5x6fdlpQ1MLlstrbHuJL3zlWzxKEdiy7mi+0wufk7xcSQ81zuVTw104HsqTGOvIj51ZEcGzJGnyuFYQc0WzxBzXEq7peWUO11e6XMQNw1aOHKU+2S9JdYCSGnHnXS6fnjfirnfK3HjmKdHoO6bT0Vg8V1/w56Yxb4lt5cvXgXAz5attB8bn89HF+vJt0OXpL5fLt1eWSa/15GJ82/I5yiKfHl0uTw/9rDMv53wb9Mf+KLedL4NB81Luv7Yu1p2nlS7ymx8j+T4LeVrz6WX5lMdOifXk8/Q6JpBPZ7t5OvN5yUdefnl6owPrY95yu3ma2pYtu/JzwPry7U+LSG+brvGSpMnh+lJeH7quM23XX67bXdf/Qa3gT70BaeKoaaTG0UNsNNSs1h/8JVd+NFeoA+6+t8MWWjSjaCtvpnkcS9L84w4ydyWHrfWd1DVzWf7AhzQLCBz379+fgrJBm1RMM/JAXsjTtAXFkqTpwHMkNPXLm/H1QxMKgmIqXcZlYCxNMdpT8e13vt7wsJDim/w4D6rOp6hliJpjSdLi4AFtnpca5FkjKl2oYeYcPolKF5tSaF7QUD7/WcZpvHUulbqaU9iUQpKWBwNjSWq0BcZdwbIkaemxKYUkSZJUMzCWJEmSagbGktSI5hLL6QG8Tz/9ND0YSZ537txZffnll9VDDz1UrV27No2jo59xTJOkpczAWJKUECTzc+Tbtm2rDhw40IytUj/jrr76aoNjSUuagbEkKaHGmFcf/fKXv6wOHz6catAJijds2JCm7927t3rsscdSvyQtRQbGkqQ5u3btqm666aZq5cqVafiMM86onn/++bng+MUXX7TWWNKSZWAsSX0sl1e1EfyuW7euGfoaQfJJJ52U+j/44INUmyxJS5GBsSRlluMDeIHgN2qKS2eddVbTJ0lLl4GxJCkx+JW03BkYS5IkSTUDY0mSJKlmYCxJHZZjO2NJWs4MjCWpsFzeQiFJOpqBsSRJklQzMJYkSZJqBsaSJElSzcBYkiRJqq044lMmktQq3krhaVKSlgdrjCVJkqSagbEk9WBtsSQtHwbGktTBoFiSlhcDY0mSJKlmYCxJkiTVDIwlSZKkmoGxJEmSVDMwliRJkmoGxpIkSVLNwFiSJEmqGRhLkiRJNQNjSZIkqWZgLEmSJNUMjCVJkqSagbEkSZJUMzCWJEmSagbGkiRJUs3AWJIkSaoZGEuSJEk1A2NJkiSpZmAsSZIk1QyMJUmSpJqBsSRJklQzMJYkSZJqBsaSJElSzcBYkiRJqhkYS5IkSTUDY0nq8Pbbb1fHH398tXnz5maMJGkpMzCWpBaffvpptXHjxurzzz9vxkiSljoDY0kqvPvuu9U///M/V++//34zRpK0HBgYS1LmmWeeqb7zne9Uf/zjH5sxkqTlwsBYkmrUEl9++eXV9ddfn5pPrFq1KnWSpOXDwFjSskd74ssuu6x66aWX0vD3v//96n/+53+qs88+Ow0Pi/WtWbOm+tWvfpX+r1ixInUvvPBCM4ckaRoZGEtS49xzz60OHDhQPf7449XKlSubsaO78847q+eee646cuRItWPHjurGG29Mb7qQJE0nA2NJy96JJ55Y/dd//Vf15ptvVmeccUYzdnx33XVX9a1vfSv1r1+/Pv3/5JNP0n9J0vQxMJak2gknnND0Tc7atWubvq/Wf9xxxzVDkqRpZGAsSZIk1QyMJUmSpJqBsSRJklQzMJYkSZJqBsaSJElSzcBYkiaM178dPHiwuvLKK5sx7eP64UdBNLvcf9LsMTCWJC0ofuTk+OOPT4Ej/6f1R09mJZ2SJsfAWJK0YPi57I0bN1aff/55tX379vSfYcZPk1lJp6TJMjCWpBlFkLZmzZrq4osvrr788stm7HTjZ7Lff//9asOGDdU999xTbdq0KQ0zfhBRi7t58+ZmzOBi2UHKa9x0SppNBsaSNCaCrFtvvTXdcqe7+eabFyRQjeBt69at1cqVK5ux0+uFF16onnzyydQfaeb/qlWr0vidO3emab3wE9tXXHFFmp/1DePVV19NNb8nnXRSz/KaRDolzaYVR2pNvySpQWB79dVXV3v37k21hU888UQz5VhRe/mLX/wi/Sdw2r9/f/X888+PFbASZHedognerrrqqlSjOe52Zg015RdccEHqf/3119ODjYNgPxHY7tmzZ6iHIEfVa/9Jmk7WGEtSCwLN3//+9ymw6RUUE6QdOnSouu+++9IydNQY//3vf6/efffdZq7JImh/+OGHU/+s1BZPEoHwli1bUm35008/3Yztjf1EEH3aaadV55xzTjNWko5mYCxJM2bfvn2pJpva4nXr1jVjl5f169enpg2PPPLIQA/EvfXWWymQpqZ50BpmScuPgbEkjYEga/Xq1am9LzW5dI8//nj1D//wD9UZZ5zRzDU5eW3x5ZdfnmqLaVYR7Zu72t2yHA+dMc9Ct5Gdj/RFW+NBa42fffbZ9P+ss85K/7tMe1lKml8GxpI0JtoW87aDb3zjG6n75JNP5q3dL80z3nnnnVRbSq0paBpAEwFEAFhqW26hzFf6rrnmmvS/X61xNKMYZN3TXpaS5peBsSSNiQCYmkPaI9NRYzxf7X7jzQpnn332XI00tdb5w2htQWLbcgtlvtLH2yUITqk1pqlEl88++6z64osvBlr3tJelpPm1bALjBx54IN32mk+nn356eiBEmk8cY88880wzNLvIg5+X4XAL/8UXX0z95SvHovaUAJBAMJcvF80vFtp8pI+glOAUf/nLX9L/NhHIDrruaS9LaTm47LLL0pfTfvhiTPw1KSkwjvZUdP0uVEyPeSeZkHGRFoLfNoy/4447mqH5wfYPHjzYDA1n1OCAgyHfd3k3Tfumn8UMjvLjuasb5IO5UNivp556anXdddel/kgjJ5Be4othdG3IJ9M4rtrk2yu7WCbW0auL/U0eyMssHattuK3OWyjip4OjI2j67W9/28w1GYcPH64++OCD1B/BW4gmAASABIK5abj1Px/pIyjlCwL+/Oc/p/+lCGSHWfe0l6W01HEOvfHGG+fu3pTxTl45xGd1165daXzX9WsoRxq7d+/mZYup61IHfnPz7N+/vxm7+CLta9asacYc6/777++Zt0lg+7fcckszNBjSPuwypTJv7JvYT9OOtF566aXN0OKhrCjHEvuHbhpwnJRpzPc1n88uMU+vvLAfusohxPbyz3+sOx9HWrs+j+X+ZnvTcAyMgvKsA6Qj9Yn5yIEDB9K4Ong9sm3btrly2bFjRxo/CpbP/elPf0rbY/yePXuasV/btGlTmrZhw4aUjkAa2sYvtPlIX79lo8zYR4cOHWrG9jeJtDKfpOFwPcivVVzb8utJxDzlNS+uT+M6qilFnZj0v6vmtT4hVPUFrxmaHnxTqAsq1djO2i3m66+/vumbHL5hUR7o2pfTgm+E04xaTbrFRk3sK6+8Ut1+++3NmK/F55bPZxuOgX6f2/iWzbqGvbtSn4zS/3vvvTf97+ell15q+r5Cnt57772Z++zy88I//OEPU60iteDR1pRazO3bt889wDXo68QGwUN9bI/ayqgpzUUtMjWa1Gximm79z2f6qEmvA9Vm6GvRjILz4jCvaZv2spSWorgOlNddrhEhroO8Pz7HZ5xr3bh3oY8KjLkt1OvCyIX5hhtuaIaORmaiiru8NVpWgee3fWMay+e3ewe9fR3zRUERJPeSp6VMZ2w/2rWUF+pYLubphen5NvLtsp0YxqOPPpr68+3FvHSDlkXulFNOSf8//PDD9B+sJ19vjgOJtEa68vxFXujaDriYRpenNcoTrJv+vMwZ5svMyy+/nPqZH/mxlI8PeVnStYlt0/XbV11iP+VIP2UQaYx56Gcc0+iPcijLvMwLaaOL+bo+0ASd3K5vQwDGyYDjqEwveBCMQK0Xgmq+pMQXlfLY74VXlSE/cbUhbWX+A3nr99mdNhGktskf4FpIbU0ApunW/2KkL5pYlE1P+pn2spSWorvvvvuYCrOoZAhcS4hV286xxKhcC8dxzMN3JArlBYzhrgsz07ioHTny1RPZyIORNWvWpBpMpu3evTsFQgQCZI5piO0yDxketPaJ91fGsgQHrLstOAiXXHJJ2gYBGV2kk/Rw8mMa67vwwgvT+EDQQtqZTsd28iCvRK1YXkvHjmW5EMPkn/no5xsSaWdbpI1xlBtp6ZWnNh999FH6TxtOEOhwsLFOOso40k8wFgcS/VHbDObhAsAy1AwyXwRNvdLKMRFfsFgHX6qizGP5SAcd/Xy5YT9Qi862GEfZ5F/UWDdlFvuCbZKGPKjkP18ImE7HvuoKOHuJi2EgH6SfNLz22msp3f/3f/83dwzzGbjoootSP8gL5RHlQ57IS6SFY4+0geM9P15KzBdfdtpE4FvWGlPWHPP9sH/y2vFhgtT41t7r84A333yz6TvW+eef3/ezGyi32Odd3SCBfdtyvboSgdO5557bN0hqe4BrvuQBOTWb1HByHBPcDfsGBWrEy3bT0XHMj1ILPsn0DYI08jnkfMv+GsZCp1Va7jj/c708+eSTmzHHYh6uaeWdxxCf2UGuAZ3qC3ZCe446yEj99QX/mHYaMRxtOPgf6pPkUW09WA/zxDj6o70I49qGY9ugn3X2w7L5fG3rCpGmHGlgHMuRn3xdTIs0tqUnlo15wDx1cNMMtbezLNNXLsO0fHqvPAWm5XmLZfJx7NN8n0X6YxxpYJhlQ8yTI72Rln5pjXTl68yXB+miC3F8xTKRhnI4x3C+H8rpkbdemN7W5WkH6S/3aeQ7348gX+W4spzbPmulKJN8/wXGxTbKdYO0MhxpzMsplPuxbb+FtrSwjXJcpKXs8u3kIn1teZw1tGmtg/e5PNeBcxo3CpbP0a643zrLeaKtLG1jhxFtc1m27OpAc6j2urlJpS+wXFeaYltsYxTjppV5JQ2mvN6X4noZXZcyzhhW6+vaylpj/tcbSf2liPDrhMzVJkQNX9Qk1dtJNVGsh/nalLVhrLOf55577qiaZWoF6oJLt44HEd9KSCffMtgm6afmK689o5agrA2LaVEzOylsi/LLa2aQN4noUi5DuQdq46i9jHmibfPHH3+c/oPlKMNA3sr9xe3yaLYyaFrzdaJXXtgPpJtlqBnsaoOd1yyy3dgP1A4h0kQXteH9aiM5xtl2dBxLbbpqYPPaYlDmUWMfoilSfDbQtZ1hlbXGUVtcln+Jz0vsU2zcuDH95/PVJT+W+NzUAe3cN/XAfsnLsz7pNVOWHmoTH3rooVTD+qMf/aj6yU9+UtVBVDN1cuK9vdRa0pSjTd4EgHf7xmdi7dq16T+1qOybrl91C/yy3N/+9rej9mF07PNh2uvm+qUPg6YR0VTim9/8Zvpxldygv3bXZVJlKWl81BJz/rmlubs6yp3gQbQGxlzguFhHgMuFMy6WXThRlifPuFASEHMBxSAB76BIH4FTXKDpCEbYxijV6FHgLM+6xqqKH0MZoNHxAE8/5TIlgpdyngjwRzVqWnuJW+V8QSuDKdLLhSiCNi5U7K/yi1WZJrp+AWJp1toQkj+O32hrTHOIrmcCAsd4HO/RUb6Iz3+b8lgqg+I25513XtM3nkk1pZgUtsUXII7VP/zhD+m2O0HlfDjhhBOq4447LvV3vbc3bwLw4IMPpqYcHBvRlIDp7PMrr7wyDS+0funDKGks3+sczSj4IjHqZ3nay1JajogxJlWh1KY1MEbUGlNT2qvWKcaX7Qe5WHBSouMCy8kjr5UaV9Ri5xfn6DBIG0lqSwkCOPGRTtJMgbMOAowoA/JYtn+MftpGdilrCwfBtqiJLY37zYh8vvHGG83QVyLPXQg22W9lTWvcSZiPtLJuaqXZB13BFncJoqaamkuOgwjw40Ew8pZjvf1qjEscr+SR/Ay7LCjz8u5F1NB35a1NzJvX7neJWuOo1e63HY5x9nH++aGLLySTDDApy2hHHsdQiBr02H+9RK1Br27cL3yDoJZ48+bN6cv5FVdckc6B893ulBpRakbR9d5exINmBw4cSLWdHAcEcdNiUumL4BdlrfAwv3bXy7SXpbRUROVJfke1C9eTrhiLa1qvZ3L66QyM+fATkbOBfk+0E5hwcciDBwJT1hEX88horwdwBsV2CIy6Am3SQyDbdlGPcayDNOdNMfI8UODRfCJqQPNb6NyupnzIYy95YBkBI2mPwIBt5NMpa9KeBw6kubxNP6wIJvOAkQcXe9XiRe1snu9IKyaRVg7ueJsB+aeZBWUWeMgtR/o5tvIgKD8OWB9fagiYcwTwTBtWBNSjLEs6yUv+RYFAlONzWBxrZVm0ifyz3fhi14W8dX3pjX3PZ2KSKEv2TXkHii9t5HGUcl4sjz32WPXkk0+mNN93333N2PlFjSivCQNNKQjO20QTgBDBHabh9n+v9GHQNEbw21YrzGeeQHbc16pNe1lKSwWfMz5PZSVeiTiAO6NtMWDEOGNVjtSBxVxD5ujqiyqjj3qwB3WCj5qP4VBf7I+alj9Eky/H+qK/XB/ryKfTRVpCNM6OjuFcuXyexnLZfN2kl+l5mkr5snm5IJ9WX+CbsUfnPbZPPkOepkC6Yhxdua1QztdrXpT5ZxikNx9fyqfl5YmutJb7oZwvyohyz+dpW1/0k962PNOV6WrbfptyvrYuyikf15Z+unzfopwe60J+bJTpL7Gecp58eTrmAXnNj8Fy/9INs8/pyFe5vbY0l/lt6/K0BdaVl82044cd+IEH8tP1Qw/xoFY8uDUKli/FQ3H91hvbry82Rz2URj/jeLBsMXWlD4OmMR68K/dB7J9xyj43alm27T9J3bgOlNeIMr5su4YErul04/BTq5lTBp8gIGsbv5TwYV+KeWw7EU67CIjagiXkgfOkA+N83b3ejhBBI0FdbtCgc751pQ+DpHHQcpiEUcuSZSQNh+vBKBUlUXE2rs6mFNI04sGrtgdBaZs6TpuiWUCTHm4P500zZh3NOWjy0fVOymmVt/Wlecj3vve9uV9HozlRfWKv/vrXv6ZhbufT7IHpvLliXDQL2Lp1a+qP9+uWGMc0lM0UpsEk0hc/tlEHpn0fMh3HtJeltNRwPeC6kDfT7IcmFGvWrDmqKeaoDIw1U2jT3BYY0lZ2IR64WmycMGj/3tZ+ftaQB9qU9/vFvGmUB6fYu3dvdeaZZ6YHQn/2s5+l9uXf/va3m6lVddVVV1Xf/e53J3aMrlu3rtqwYUPa7r59+5qxX4ugkXmYd9pMIn0PP/xw+tLB+WA+H4ab9rKUliKuC1wf8ueiulA5ET9gxhflcRkYa6Zw4POByV/NRTeLwdWoeOBgKXwJIA/jvtpvMfGKrt/85jcpIMaqVauqH//4x6lWn7cgEDjzy3jYtm1bGj+pAC4PzAkQ81pjArnbbrttIg+ezYdJpC/eRjHftcXTXpbSUsb1od8LDsB5YKIxQGpQIUmaOv1O0fFQGG1cox1sdLS/zR9IC4vVxnjQ9KFfGvN8z4dJlSXLSpot1hhL0oziNXHUllBrzA9chGuvvbZ66qmnpqqGM/9lu3HS9/bbb6ea+jo4nrcf1phUWiXNHgNjSctWvIeW5jg7d+5MTRJ4QI7AKJrp0M+4vLnCtKBpBg+b/P73v09NU44c+erd3r/+9a+n7kcoCGInkb74qeonnniiGTN5k0qrpNmzov7gc7tHkpYdAmPasPHwxq233pp+ROWPf/xjM/VoPHz1/PPPL2jNIYG5p+jZ5f6TZo81xpJUo8aYAPmXv/xldfjw4RTQ8DPABMTgDRD82p0kaekyMJakBq9Zu+mmm+ZqhXm7BLXEERy3vTd4nJ8Fpr0sP8vOOtpY2zjb3H/S7DEwlqQawW/be2oJkuPBtg8++CDVJueine+wD4IRDPNjNbwKTJI0HQyMJalG8NvVfviss85q+iaD2mV+rZGmG5Kk6WFgLEm1UYPfYZtSMB+/hLdjx45qz549zVhJ0jQwMJakBRSvAuMtGJKk6WJgLEkTsnnz5rn3H7d1TJckTS8DY0maEH50gtrgrm4+f5RCkjQ+A2NJkiSpZmAsSRNiUwpJmm0GxpI0ITalkKTZZmAsSZIk1QyMJUmSpNqKI9zfkyRJkpY5a4wlSZKkmoGxJEmSVDMwliRJkmoGxpIkSVLNwFiSJEmqGRhLkiRJNQNjSZIkqWZgLEmSJNUMjCVJkqSagbEkSZJUMzCWJEmSagbGkiRJUs3AWJIkSaoZGEuSJEk1A2NJkiSpZmAsSZIk1QyMJUmSpJqBsSRJklQzMJYkSZJqBsaSJElSzcBYkiRJqhkYS5IkSTUDY0mSJKlmYCxJkiTVDIwlaZF8+umn1Zo1a6oVK1ZUt912W/rPMOMlSQvPwFiSFsmdd95Zvf/++9WGDRuqe+65p9q0aVMaZrwkaeEZGEvSInjhhReqJ598MvVv3bq1WrlyZfq/atWqNH7nzp1pmiRp4aw4Umv6JUmSpGXLGmNJkiSpZmAsSZIk1QyMJWmB0b6YN1DQ0d/myy+/rC6++OI0j+2NJWlhGBhL0gI755xzqtNOOy31P/vss+l/6d13363eeeed9DDe+vXrm7GSpPlkYCxJC+zEE0+sLrjggtT/+uuvt763+NVXX60+//zz6uyzz67OOOOMZqwkaT6NHBg/88wz6RYf79ycRZNO/6yXR+n000+vtmzZ0gxNBwIIyji6QTDfAw880AyJ45My4XidVhx3HH+zZthj7Zprrkn/v/jii+qzzz5L/YFmFC+++GLqv/zyy9Or3KbdZZddlrpZ1Hb+Zl8Oep6ZlMU49uO8yn99bdjPswYzH+U66XWOFBhzErn++uubodl03XXXVbypLm5nDosyiBPJUiiPHCfmgwcPNkPTgbK+8cYb0z6LNwz2C9xHuaixnWkOGsfBRZ9fVZtm7NNHH320GZodoxxr0ZyCWmFqh3Oz0Iwi//wREL/88svN0GzpOn/ffvvtc+eaQfU7J/WyGMc+57sLL7ywGeptnLxNEuex+Q5YR/k861htxwyfKT5bkzIf+2qkwJigcvfu3c3Q8rRr166mb+mVx3vvvTd1AdTTTz99VE0KH65HHnmkGWo37EUNbGepIgibti88JfbpLbfc0gzNjlGOtbw5BbXD1BKHaW9GQXDyyiuvNENV9dJLL1WXXnppMzRbJnX+JsjMa5yHtRjHPsff/v37m6Fu4+Ztkp577rmmb/6M8nnWsRbii9587CvbGI+AGoZZrR2ZVQtxUubkP4u1lZpd0ZyC2mFqiTELzSimpfZwmnBHa6malrxxHbjjjjuaIU2zWT5HpMCYqujoOPCibRVd1NIxPsYRQOS4jRbTShROTCsLinFsK7YXt7DzbdH1CopiXpbN012msURayrZc+bJdO5VtxG03bkExf2nU8ijlaYxlog1fXkakCXn+o8wolxgu00U+Y7hXfmOe2E4uptHlZZ5vlzzQ3yXPC12elkgjX0To6I8yaJPnKY6nXD6dLrbFvHFLkf3LtChD5smXyfNZivXzP8q73Bd05bGHfDtteYxybFu+V3nn233zzTebsV+Lfcw2SXdbufWSp4suxzrp8n3cdqzFOtryncvzQlfuo37Lh67jAPk+zPdJbCvk6xi2zEJbc4pxm1Hkn9kybTEN/M+Po3y5XuXIMnwWufvAvOX+7LevYxodZdgL85D+fD/ky8Tx0Hbc98tPvs42LFMul+9zujgm6Kc84hzFtgPriPnz8WHQYz/k+aLr2r+x3nwfh3wdH3/8cTO2HfO05a0si3xfxzT+R/6jrGKf0XXlOd83sV7WFXcyCY6ZxrhSLFcuG+NimXwb+fg2kYcoy/wYL/dplDtdvm8Q0+KYHgbLskyUX77d2B5dmY98fL5NlmedeV7a9kfkna7Ma74sXWybZaKSifGxXqbn8+XL9tpX5XbiWGqTl3+sM18+1tnTkcall1565JZbbmmGjhypPwjUTx/ZvXt3M+bIkfvvvz+NB+OZXh+oR/bv35/GMcw8gXXGcKwvhumni3lYD+tkXYwPpCkfzsU66Vg+1s066brEOlkmMH/kg/58WinSGPNj3PIo5WnkP9q2W64j0sH6o5+uTFdeRvkyIbYb6+Y/w8yLSH8sE9PL7bKNGG4T64m0xXCkLeTp7RLlE2mKMow8xLojD3maUU5HzBMol650xPbp8nyzXtaTL1euh/74/EU6It1g/nI40tWrvPN+MJ3hyCNpjnRE+mPaIFg2li/THdsirXSI9ESZI89bpCHmbxPrYN5clF8/5X5m25GmfB+ShpiH/nz9MV/kozzWhrFp06a07IYNG44cPnz4yI4dO44aHkZZvuyDKMvIJx3jmTemkf48f8zTqzyZVu6j2FaML9MS5R7DebmXYt7oYj9EOTM91k8X+aE/5uuVH/pZJsR6Ii1Mi/WGfP2I/AaG8/nB9Eh72+eL6aMc+3k6Y/58/zKOeaIc820yX74N+pmn/DzlyryV6833Zf4ZYpk8zW3pj3ljXYyLMontxDDK4TbkKU8vWH/kMdIb2uYvt8Mxk5cb2tIW24i8xjDLRx7pz7ffT+wj0hjHLtuN8onyjHzl5Rvbpz/SH9uni3GxLqYFpkWaY7+Ww7GtMk9lGcf8dJEmsI2y7PN9FcuFtrJjON8P/dbZz9zaWWkUUGBj+crzDbORMoPMG4VKYZXry3cMyvWD5aPgEYWSj8vFzszT1paXEtvJ5yEd+TbKdOUiTXneJ1Eepa4DIE9nmfdIRxysXenK89eWH9IVaQ/5cmwz3265H2K7eVrbsI08LWAd/dLchjTnaUKepkhjrLfMd0zP08yy+T4qj5tSrLNMB8vEPkHkkXGxTD6dvEb5R1nmyu10lXc5rswj68nzw/hyHb3k6QTryoeZnq8/th9lXpYv+pUxyuOB9eXppj9PR65MQ5RlOZyvj3Xl2yN95T5mmXLcIPbs2ZOWXbVq1ZE//elPc4EyAfKwSDPLhjjOQgxHXkM+D8hvOS7Xto8G2dd5+cT0rjIrj1WUy0R+83nAuFyen9i/rCvEevJx5TFW7vOyrMv52U4+DNYR41hXWYZt5Zort8k62obzfOTpjvLL93+URz6uVOatXE+5jhjOywvl5yjmi/Tyv8w/8+fj2tZbKssJ+TL05+tsK/dyO/3mYZvMk2N6jON/vr68HAbBtsvts758nbFfYhzbIF0h32ZbfhgX5ca+KdPI/DGO/3l+y31JGmJdIebhf+i3r9hGnodYRz6O4XyZtu3kae1nro3xxo0b0+2SqGamur5eUbp9QjV0V9X16tWrm76vxHzcsmV9UX1NF7ffcuWtQh7oiFvZdHF7+6OPPkr/u5xyyilN31fK7fRDOmK75J2HSUYxbnmU6gOx6ftav7JoU6ZrFNzy5cE8cNs3bmfRRTo//PDD9D+cd955TV879nf5ZhCORfS7xZejnCnL888/vxlzLLZTH/PpgRNusQzyNDZPz0aeyeegbZDzdETaKKMor2gnd+jQobl85uXA8RcPF7722mvHHAfkgXG9yjs+y732AeuJ45JbUDyIRDeoSGfc/mo7nllvKfLMcXTJJZek/mHQ5jHOTeChyTzd9Hc9nDnocXDyySc3fV+J42CQY20YeXOKt956a26/rV27Nv3nHcfs665fyMuRb/IGyr2rPSZ5D7G9ODbp4jiP8h1Uv309yDmjF8pp0OO+Kz9vvPFGWkf+eRsE+zy/zuRl3YbtRPOD6FhHfg4d9tgfdP+WeYvyiqZU+f4fxaCfobbPSOQfcV3iPIhRr5OlOBdEUwb2e77vRj2v98J5mvXkaUd8hi666KK5458mCaPEGOXx0u8zNWxcQxoRn5Nexy//Tz311NQPjgWOiWE/V/321SgxIWmhLOJhetaZp7WfucCYzNTfAOZWxE7evn176udgpRv2QkDCKKiy66f+JnDMMpN8vUcb1h8fPnZC2wl+XKOWx7Sqv6Edk5euYGRacBLnw8UJoP422YztLT6Q5I8vi6Pi+CrLa9wL1CREvuKCFCeoQURAfO+996b1xIl5vnEyZVvxhHpcfAY1ynEwX/K3Uzz44IPpvcacjwmYwXT2zZVXXpmG++HcRd64oPAZHVR+XEY37EWun4U8Z5TboZt0fvrhmlqmIQ8MRzHq/p20UT5DxBQcy/HlJT6/ecXNpK6TnNPi7VFsJwKwQNrpWPc45/Uc6ynTHcEo22cYEcxOQq/P1LhxzXwcv2367atRYkKuSfGFh3UOE78e9VYKvl3wYeOg5WCPYJkEEygPcxGntoUdUl6w+DD1wo5jWznWUTb6njTSRX4pcNJNN0yA0M+o5dHPQgUiIO0333xz6qes+LZaGjY/7O/y23rUHvSrbc7FBY9vuV3Yn2yLfVx+8Lrw0ECc7EYVaYvamkB6+KxFzWRcLEIc83zT5dgppzMuvuG3iYtNud0c6yQdnEjJI3m9++67m6n98e2dk9YotR+gbDjnjIJjkQsM6c+fmuc45aLTdc4Y5TjIDXKsDSveTnHgwIFUc8y5loB4WHz++EyRt0hnP3GctB1/5flqHKRnEueMQY/7rvxQG9V2Lu6Hc215bWIb5XYC26HGrRT5HeXYH2X/5rrONcMa9TNEmjnHcN7gM8rnl6A68jLJ6+QNN9yQyr8tr6Oc1/vVODK9bX9G2uM/24ztjhvX9PtM8Z952B7lSse+60ItLMc5y/Q7fjkOy22z33qtv0uvfTVqTBjHJfORzmHi16MC42hOwUUmVhq3LIdFIgiqy6r/ficjLsp84PLCHTbaHwXpih2ef0jbxImXW4Skc5CTzKjlMYj84ORbITi4x113HrBycHEcxLc0vvkznB+clEWvC1ab+GYbT62Cb3qctGI/DIplONHG/ohjiHHs27j1EuVSBjaxPeZjHSzPt+O8HEcN4vhWz77J18UXTo6LODbK25FxO4wyZ3/m0yl3xvW6KJEf1ptvN2poGBf7Lp/OyZ0T0SBimbxcOX8MgxMiy8RnDxx3jOtXoxJNbkh/Xg7ku1dNWr/jYBD9jrVhRXOKEIEyhmlKQZ7yGp22i2aJ7UawkmPZPE25+LKGQfM76jkj/6LGtgY57nvlJ5bNz8VRW8W6u87nUQMV+xrcYY0LLuuOsiedbIf15ee2OOYwyrE/yv7NkVbSlJdN3CVmXJ63XJm3UT9D7HuOnQgO6aL8EOfCXtdJ0s+5kXH5sVSKvBLDlPHDqOd19k0sF/uNzzzpiNrZfH9yLOUBdX4skLa8ycAo+n2mSGukJz7LeVxDemOfMy954ThHv+OXz2W5ba4vUaEVecvT0KXXvhonJozz9NBv96kPzKPUiTuqETOYLW/ozHTGRUcDZ5aLYfpDfZAfNS/qnXHUuDrxaXxgffn0Mj2hbT7WlY9jW6W2NLEceYxxXdsMsQ6WG7c82pTzdZVZWQaRB+YfJF15nuny/Zyngf5SV5ratttLuZ687Mv8ldNLeZrjWMjnj2n59HyeGBf5LcsnX6Y8tnqVJfqVS75v6Er59Hx/DLPeSH+kjXnpz+cJkZ+2fR/y8mAdUf705+ukv9yXUeZlubFO5h8E89KVWHesv025vejP00xH2vJjii7k42MdvbbZTzx0V1/Ajhw6dKgZeyT1M46H9Popy7hX3srjN5+3bXou/8yyzUH3dflZb9t3IeZl2Zg/Py7y8XRsM9crP23pyOcp8xPKbcbnCHme8211rQvDHvvD7t98OP8c5+NjHWX55dryFsN0vdKRl1GZ3+hYPtf1mUPsg17lFJi3bb62co/+ffv2HTMt5HmLdbCNXEyny8uc9eTlmK838puXVS6WocvXibZjOdCf5zVPK9PID/9jetv28zyXZVmWY1dZMF85b7mtrn2F8riP7fTKO2J6HLMhxndtbwV/6hkkqRU1AtTORo3DNIlahLIGMWox+rVDmyY7d+5MNUB1gFw98cQTzdivaoypUfn5z38+cDvjpYCapvrClZrq9Koh1uyI2vi8lhh8XqkBLMcvN9SsLtRzOmyLmvL8DsRSRI33sE39jmpKIUmlXrfUFxu3wMugiYsvt8/oum6LT5v81+7yZhTSUsKt8rbgl9vu+QN4y1HZ7ELjo0yHbkZRMzCW1IraWNo6LlQNxrA46bUF7Fx4uRFGNys1UPFrdxs2bKjWrVvXjJWWDj6vBw8ePKpNKhjPw1XT+uV7IVCrSdveWbrDNQso03gWZSg0pZCkWcFpi65sazerDhw4kH7ljjy1/ajHMG2Ml4quNoWabWWbULql8jmeJdGmObqlhHNF5KtXu/lebGMsSYsg2hQHaouff/75auXKlc2YryzXNsaStBhsSiFJiyB+2Q7XXntt9dRTTx0TFEuSFpaBsSQtAmp/uWFH9+tf/3qkH/SQJE2WTSkkSZKkqqr+f2NuhFG7W9ceAAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Data collection\u003c/h2\u003e\n \u003cp\u003eThis study used an artificial neural network to predict the mechanical properties of polypropy\u0026zwj;lene (PP) blends with recycled polypropylene (r-PP). The properties analyzed included tensile strength (\u0026sigma;\u003csub\u003eT\u003c/sub\u003e), Young\u0026apos;s modulus (E\u003csub\u003eL\u003c/sub\u003e), elongation\u0026zwj; at break (e), impact strength (F), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e). In the ratio of polypropy\u0026zwj;lene (PP) to recycled polypropylene (r-PP) gm equal to 0:100, 20:80, 50:50, 80:20, 100:0 by weight, DCP catalyst was added in the ratio of 0.1 phr and VTMS A-171 coupling agent in the ratio of 0, 1, 3, and 5 phr. The obtained mixture was oven-dried at 60\u0026deg;C for 2 hours to remove moisture. The mixing was carried out using a co-rotating twin screw mixer with an L/D ratio of 40 and a screw speed of 150 rpm, resulting in a production rate of 1 kg/h. The mixing temperature varies from the feed zone to the pressure zone, with temperatures ranging from 180\u0026ndash;200\u0026deg;C. The material properties were determined using the following standards: Tensile strength (\u0026sigma;\u003csub\u003eT\u003c/sub\u003e), elonga\u0026zwj;tion at break (e), and modulus of elasticity (E\u003csub\u003eL\u003c/sub\u003e) were measured according to ASTM D 638 Type I, impact strength (F) according to ASTM D 256; and Shore D hardness (H\u003csub\u003eshore D\u003c/sub\u003e) according to ASTM 2240. Which the results of the mechanical properties were analyzed for correlation between the variables as illustrated in\u0026zwj; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Multiple regression analysis\u003c/h2\u003e\n \u003cp\u003eFollowing the assessment of the material\u0026apos;s mechanical characteristics, the resultant data will be subjected to analysis via multiple regression, a statistical technique capable of elucidating the nonlinear connection between independent and dependent variables. Consequently, it is employed to forecast the mechanical characteristics of composites, as seen below.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere is constants, x is explanatory variables, n is the degree of the polynomial, and e is the error value in the prediction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Artificial neural network model\u003c/h2\u003e\n \u003cp\u003eNeural networks commonly require training with input parameters and their related outputs, necessita\u0026zwj;ting a substantial dataset for optimal performance. This research involved the creation of a dataset\u0026zwj; derived from mechanical property test results of polypropylene (pp) blends with recycled polypropylene (r-PP). In modeling using ANN, there are 3 input parameters selected for this modeling consisting of the proportion of each type of plastic and the silane additive that the network has 2 hidden layers, each with 20 neurons, which the output consists of 5 nodes, including tensile strength (\u0026sigma;\u003csub\u003eT\u003c/sub\u003e), Young\u0026apos;s modulus (E\u003csub\u003eL\u003c/sub\u003e), elongation\u0026zwj; at break (e), impact strength (F), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e), For neural network training, numerical data undergoes organization into training and testing sets, a\u0026zwj;dhering to an approximate 70:30 ratio [7\u0026ndash;8]. During the training phase, backpropagation, enhanced by a spe\u0026zwj;ed-up algorithm, refines network errors and parameters through the Rectified Linear Unit function. The procedure of the proposed ANN model is summarized in Fig. 3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Performance evaluation\u003c/h2\u003e\n \u003cp\u003eWhen assessing neural networks for predicting mechanical qualities, suitable metrics are necessary to effectively represent the model\u0026apos;s performance and dependability. These metrics must account for the attributes of the material data and include Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). given by equations (1)\u0026ndash;(3).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Results of Mechanical properties test\u003c/h2\u003e \u003cp\u003eThe mechanical property assessments were performed in accordance with ASTM standards. The mechanical property test findings of PP and r-PP indicate that an increase in the quantity of PP correlates with an enhancement in tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's modulus (E\u003csub\u003eL\u003c/sub\u003e), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e). The impact resistance diminisheswith an increased fraction of r-PP, attributable to the deterioration of the polymer structure during the recycling process. Materials composed of 100% r-PP have significantly elevated elongation at breaks (e) and superior impact strength (F) compared to pure PP plastic, signifying enhanced durability and impact resistance. Although the mechanical strength decreases, the use of silane helps increase the Young's modulus (E\u003csub\u003eL\u003c/sub\u003e) and hardness (H\u003csub\u003eshore D\u003c/sub\u003e) [37\u0026ndash;39]. Especially as the percentage of PP increases. However, when the concentration of silane reaches 5 phr, the elongation and impact values decrease significantly because the high amount of silane makes the material too rigid, reducing its flexibility. Additionally, a blend consisting of 80% PP and 20% r-PP provides an optimal balance between strength and elongation, as seen in Fig.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results of Multiple regression analysis\u003c/h2\u003e \u003cp\u003eTo investigate the influence of the two factors under investigation, the predicted mechanical characteristics of polypropylene (PP) blended with recycled polypropylene (r-PP) were analyzed utilizing a polynomial regression model, with the coefficient of determination (R\u0026sup2;) employed to explain data variance. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates that the polynomial regression equations have R\u0026sup2; values over 90% for tensile strength and Young's modulus, signifying that the model proficiently accounts for variation and can be used for prediction. On the other hand, the elongation at break and impact strength have R\u0026sup2; values below 60%, suggesting that the model can adequately account for the data variability. The hardness exhibits an R\u0026sup2; value over 60%, signifying that this model was appropriate for prediction with intermediate to high precision.\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePolynomial Regression Formula\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression equation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTensile Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.419\u0026thinsp;+\u0026thinsp;0.17246A \u0026minus;\u0026thinsp;0.1797C \u0026minus;\u0026thinsp;0.000731A\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;0.0141C\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung's Modulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317.4\u0026thinsp;+\u0026thinsp;6.189A\u0026thinsp;+\u0026thinsp;8.7C \u0026minus;\u0026thinsp;0.03486A\u003csup\u003e2\u003c/sup\u003e- 2.04C\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElongation at Break\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.1\u0026ndash;2.366A -\u0026nbsp;33.0C +\u0026nbsp;0.01833A\u003csup\u003e2\u003c/sup\u003e +\u0026nbsp;5.10C\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.603\u0026ndash;0.1093A -\u0026nbsp;1.010C +\u0026nbsp;0.000834A\u003csup\u003e2\u003c/sup\u003e +\u0026nbsp;0.1565C\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHardness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.365 +\u0026nbsp;0.1544A +\u0026nbsp;1.115C -\u0026nbsp;0.000888A\u003csup\u003e2\u003c/sup\u003e -\u0026nbsp;0.1190C\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNote\u003c/b\u003e: (A) was polypropylene (PP), B was recycled polypropylene (r-PP), and C was Silence substance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed that the formulated equations may effectively predict some characteristics; nevertheless, the omission of other variables may result in interpretative inaccuracies. This has resulted in the application of artificial neural networks to predict the mechanical characteristics of polypropylene (PP) blended with recycled polypropylene (r-PP).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Results of Prediction by ANN Model\u003c/h2\u003e \u003cp\u003ePredicting mechanical qualities in neural networks will involve an input layer, many hidden layers, and an output layer. The Adaptive Moment Estimation (Adam) technique was employed to update the weights in the neural network during training, and it was processed through the Rectified Linear Unit (ReLU) to enable the model to learn non-linear correlations, as shown in Fig.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eThe predictive capability of the neural network model was evaluated by providing the correlation coefficient (R²) for each characteristic as follows: The hardness has an R² value of 94.91%, indicating that the model can accurately predict the hardness of the material. The elongation at the breaking point has an R² value of 81.1%, indicating an acceptable data distribution. The value of the Young's modulus shows an R² of 94.1%, indicating good predictive capability, despite some data dispersion. The impact strength has an R² value of 94.3%, indicating a fairly accurate prediction of impact strength, while the tensile strength shows an R² value of 99.5% [40–41], as shown in Fig. 5.\u003c/p\u003e\n\u003cp\u003eFrom Fig. 4, the prediction results were compared with the experimental results of the mechanical properties of polypropylene and recycled polypropylene as shown in Fig. 6. It was found that the values of tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's modulus (E\u003csub\u003eL\u003c/sub\u003e), elongation‍ at break (e), impact strength (F), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e), obtained from the prediction, tend to be close to the values obtained from the experiment, indicating that the prediction of mechanical properties using the artificial neural network was very reliable.\u003c/p\u003e\n\u003cp\u003eTo elucidate the effects of the examined factors more effectively, a contour map has been generated. This diagram only presents the attributes derived from mechanical property assessments, including tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's modulus (E\u003csub\u003eL\u003c/sub\u003e), elongation at break (e), impact strength (F), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e). Figure 6 shows that as the amount of PP increases, the values for Tensile Strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's Modulus (E\u003csub\u003eL\u003c/sub\u003e), and Hardness (H\u003csub\u003eshore D\u003c/sub\u003e) go up significantly, especially when PP is 50% or more. This can be elucidated by the observation that the molecular structure of pure polypropylene (PP) demonstrated greater hardness and strength compared to recycled polypropylene (r-PP). Nevertheless, increased PP markedly reduces the Elongation at Break (e) and Impact Strength (F) values, indicating a deterioration in the material's toughness balance, resulting in increased brittleness despite enhanced durability [24–26]. Adding Silane in the right amount, especially between 1–3 phr, slightly improves the values of Young's Modulus (E\u003csub\u003eL\u003c/sub\u003e) and Hardness (H\u003csub\u003eshore D\u003c/sub\u003e). Silane functions as a chemical intermediary between phases. Adding Silane significantly reduces Elongation at Break (e) and Impact Strength (F), especially in the high R-pp group, making the material too stiff and weakening its toughness. The research indicated that increasing the proportion of PP and including silane improves specific mechanical qualities, although it adversely affects the material's toughness.\u003c/p\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e4.4 Results of Neural network performance validation\u003c/h2\u003e\n \u003cp\u003eTable 2 presented the accuracy statistics of the neural network model in predicting the mechanical properties of a composite material composed of Polypropylene (PP) blended with Recycled Polypropylene (r-PP), which includes mechanical properties such as tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's modulus (E\u003csub\u003eL\u003c/sub\u003e), elongation at break (e), impact strength (F), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e). The model will be assessed using MSE, RMSE, and MAPE, which quantify the discrepancy between anticipated and actual values. The MSE and RMSE values were determined to be low in predicting tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), impact strength (F), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e). This finding suggests that the model possesses significant promise for predicting stable mechanical characteristics [8]. The elevated RMSE value for Elongation at Break (e) indicates that the model may not completely comprehend the intricacies of this characteristic [41–42]. The ANN model works very well, accurately predicting the mechanical properties of the PP:r-PP composite, as shown by overall MAPE values less than 10% for different parameters. Nonetheless [43–44], the Elongation at Break (e) value exhibits significant data variability, resulting in diminished accuracy of the model's predictions relative to other attributes.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe performance of the ANN neural network\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaterial\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerformance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTensile strength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYoung's modulus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElongation‍ at break\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImpact strength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHardness\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\" rowspan=\"3\"\u003e\n \u003cp\u003ePP : r-PP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e813.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e853.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis research employed an Artificial Neural Network (ANN) model to predict the mechanical characteristics of polypropylene (PP) combined with recycled polypropylene (r-PP), including tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's modulus (E\u003csub\u003eL\u003c/sub\u003e), elongation at break (e), impact resistance (F), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e). The experimental findings indicated that the mechanical qualities of polypropylene blended with recycled polypropylene improved with the increasing proportion of polypropylene. Nonetheless, impact resistance and elongation at break showed a tendency to decrease with an increase in the r-PP ratio. The performance in terms of strength and elongation was satisfactory at a mix ratio of 80% PP and 20% r-PP. The results were juxtaposed with the mechanical properties predicted by the ANN, revealing that the predicted mechanical properties closely aligned with the experimental values. This indicates that the model effectively predicts mechanical properties, as the R\u0026sup2; values exceeded 80% for all properties, and the MSE and RMSE values were limited. The MAPE values for tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's modulus (E\u003csub\u003eL\u003c/sub\u003e), and impact resistance (F) were below 10%, signifying a high degree of accuracy in the model. The MAPE values for elongation and hardness were significantly elevated, indicating that the model could properly predict the material's mechanical characteristics across several parameters.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to thank all lecturers in the Industrial Engineering Program, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, for supporting this research.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eF. Tavazza, B. Decost, and K. Choudhary, 2021. Uncertainty Prediction for Machine Learning Models of Material Properties, \u003cem\u003eACS Omega\u003c/em\u003e, Vol. 6 : 32431 \u0026ndash; 32440.\u003c/li\u003e\n\u003cli\u003eD. H. Maulud and A. M. Abdulazeez, 2020. 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Using artificial neural network models in stock market index prediction, \u003cem\u003eExpert Systems with Applications\u003c/em\u003e, Vol. 38(8) : 10389 \u0026ndash; 10397.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neural Network, Polypropylene, Polypropylene Recycle","lastPublishedDoi":"10.21203/rs.3.rs-6615547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6615547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research employed an Artificial Neural Network (ANN) model for predicting the mechanical characteristics of polypropylene (PP) combined with recycled polypropylene (r-PP), including tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's modulus (E\u003csub\u003eL\u003c/sub\u003e), elongation at break (e), impact resistance (F), and hardness (H\u003csub\u003eshore D\u003c/sub\u003e). The experimental findings indicated that the mechanical qualities of polypropylene (PP) combined with recycled polypropylene (r-PP) improved with the increasing proportion of PP. Nonetheless, impact resistance and elongation at break exhibited a tendency to diminish with an increase in the r-PP ratio. The performance in terms of strength and elongation was satisfactory at an approximate ratio of 80% PP and 20% r-PP. The results were compared with the mechanical properties predicted by the ANN, showing that the predicted mechanical properties corresponded closely with the experimental values. This suggests that the model effectively predicts mechanical properties, as the R\u0026sup2; values exceeded 80% for all properties, and the MSE and RMSE values were limited. The MAPE values for tensile strength (σ\u003csub\u003eT\u003c/sub\u003e), Young's modulus (E\u003csub\u003eL\u003c/sub\u003e), and impact resistance (F) were below 10%, signifying that the model demonstrated considerable accuracy. The MAPE values for elongation and hardness were notably elevated, indicating that the model could properly predict the material's mechanical characteristics across several parameters.\u003c/p\u003e","manuscriptTitle":"Prediction of mechanical properties of polypropylene blends with recycled polypropylene using ANN methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 13:34:41","doi":"10.21203/rs.3.rs-6615547/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0fce2a6a-a620-46aa-8d28-a28a86fe8f1d","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-24T08:54:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 13:34:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6615547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6615547","identity":"rs-6615547","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}