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Anerao, Prashant, Kulkarni, Atul, Munde, Yashwant, Kharate, Namrate. (1404). A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite. نشریه هنرهای کاربردی, 12(2), 261-270. doi: 10.22075/macs.2024.33776.1638
Prashant Anerao; Atul Kulkarni; Yashwant Munde; Namrate Kharate. "A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite". نشریه هنرهای کاربردی, 12, 2, 1404, 261-270. doi: 10.22075/macs.2024.33776.1638
Anerao, Prashant, Kulkarni, Atul, Munde, Yashwant, Kharate, Namrate. (1404). 'A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite', نشریه هنرهای کاربردی, 12(2), pp. 261-270. doi: 10.22075/macs.2024.33776.1638
Anerao, Prashant, Kulkarni, Atul, Munde, Yashwant, Kharate, Namrate. A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite. نشریه هنرهای کاربردی, 1404; 12(2): 261-270. doi: 10.22075/macs.2024.33776.1638

A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite

Mechanics of Advanced Composite Structures
دوره 12، شماره 2 - شماره پیاپی 25، آبان 2025، صفحه 261-270    اصل مقاله (694.26 K)
نوع مقاله: Special Issue: Mechanics of Advanced Fiber Reinforced Composite Structures
شناسه دیجیتال (DOI): 10.22075/macs.2024.33776.1638
نویسندگان
Prashant Anerao* 1؛ Atul Kulkarni1؛ Yashwant Munde2؛ Namrate Kharate3
1Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune 411048, India
2Department of Mechanical Engineering, Cummins College of Engineering for Women, Pune 411052, India
3Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune 411048, India
تاریخ دریافت: 25 فروردین 1403،  تاریخ بازنگری: 08 مرداد 1403،  تاریخ پذیرش: 22 مرداد 1403 
چکیده
Wood/PLA biocomposite filament is a 3D printing material that blends Polylactic Acid (PLA), a biopolymer, with wood powder acting as reinforcement. This combination results in a sustainable 3D printing filament that has grown in popularity in recent years due to its eco-friendliness and the natural appearance of 3D-printed parts. To assess the suitability of wood/PLA biocomposite for various additive manufacturing applications, it is essential to determine its mechanical properties. This study employs fused deposition modeling (FDM) as the additive manufacturing process and focuses on assessing the mechanical properties (tensile, flexural, and impact) of 3D-printed biocomposite. The Taguchi L27 design of the experiments is utilized, and the key process parameters under consideration are infill pattern, layer thickness, raster angle, nozzle temperature, and infill density. A layer thickness of 0.3 mm and an infill density of 100% yielded the highest tensile strength of 42.46 MPa, flexural strength of 83.43 MPa, and impact strength of 44.76 J/m. The dataset has been carefully prepared to facilitate machine learning for both training and testing, and it contains the experimental results and associated process parameters. Four distinct machine learning algorithms have been selected for predictive modeling: Linear Regression, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). Given the intricate nature of the dataset and the presence of nonlinear relationships between parameters, XGBoost and AdaBoost exhibited exceptional performance. Notably, the XGBoost model delivered the most accurate predictions. The results were assessed using the coefficient of determination (R2), and the achieved values for all observed mechanical properties were found to be greater than 0.99. The results signify the remarkable predictive capabilities of the machine learning model. This study provides valuable insights into using machine learning to predict the mechanical properties of 3D-printed wood/PLA composites, supporting progress in sustainable materials engineering and additive manufacturing.
کلیدواژه‌ها
Additive manufacturing؛ 3D printing؛ Fused deposition modelling؛ Biocomposite؛ Machine learning؛ Mechanical characterization
مراجع

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