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

Document Type : Special Issue: Mechanics of Advanced Fiber Reinforced Composite Structures

Authors

1 Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune 411048, India

2 Department of Mechanical Engineering, Cummins College of Engineering for Women, Pune 411052, India

3 Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune 411048, India

Abstract

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.

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Main Subjects

References

[1]    Mazzanti, V., Malagutti, L., and Mollica, F., 2019. FDM 3D printing of polymers containing natural fillers: A review of their mechanical properties. Polymers. 11 (7). 
[2]    Palaniappan, S.K., Singh, M.K., Rangappa, S.M., and Siengchin, S., 2024. Eco-friendly Biocomposites: A Step Towards Achieving Sustainable Development Goals. Applied Science and Engineering Progress. 
[3]    Suyambulingam, I., Rangappa, S.M., and Siengchin, S., 2023. Advanced Materials and Technologies for Engineering Applications. Applied Science and Engineering Progress. 
[4]    Prajapati, A.R., Dave, H.K., and Raval, H.K., 2021. Effect of fiber volume fraction on the impact strength of fiber reinforced polymer composites made by FDM process. in: Mater Today Proc, Elsevier Ltd, pp. 2102–2106. 
[5]    Kurien, R.A., Santhosh, A., Paul, D., Kurup, G.B., Reji, G.S., and Selvaraj, D.P., 2021. A Study on Recent Developments in Jute, Cotton, Coir, Silk and Abaca Fiber-reinforced Composites. in: pp. 375–384. 
[6]    Kurien, R.A., Selvaraj, D.P., Sekar, M., Koshy, C.P., Paul, C., Palanisamy, S., et al., 2023. A comprehensive review of the mechanical, physical, and thermal properties of abaca fiber for their introduction into structural polymer composites. Cellulose. 30 (14), pp.8643–8664. 
[7]    Kurien, R.A., Biju, A., Raj, A.K., Chacko, A., Joseph, B., Koshy, C.P., et al., 2023. Comparative Mechanical Properties of Duck Feather–Jute Fiber Reinforced Hybrid Composites. Transactions of the Indian Institute of Metals. 76 (9), pp.2575–2580. 
[8]    Kurien, R.A., Santhosh, A., Paul, D., Kurup, G.B., and Reji, G.S., 2021. A Review on Recent Developments in Kenaf, Sisal, Pineapple, Bamboo and Banana Fiber-Reinforced Composites. in: pp. 301–310. 
[9]    Kurien, R.A., Preno Koshy, C., Santhosh, A., Kurup, G.B., Paul, D., and Susan Reji, G., 2022. A study on vetiver fiber and lemongrass fiber reinforced composites. Materials Today: Proceedings. 68 pp.2640–2645. 
[10]    Anerao, P., Kulkarni, A., and Munde, Y., 2023. A review on exploration of the mechanical characteristics of 3D-printed biocomposites fabricated by fused deposition modelling (FDM). Rapid Prototyping Journal. 
[11]    Zandi, M.D., Jerez-Mesa, R., Lluma-Fuentes, J., Jorba-Peiro, J., and Travieso-Rodriguez, J.A., 2020. Study of the manufacturing process effects of fused filament fabrication and injection molding on tensile properties of composite PLA-wood parts. International Journal of Advanced Manufacturing Technology. 108 (5–6), pp.1725–1735. 
[12]    Kechagias, J.D., Zaoutsos, S., and Chaidas, D., 2021. Parameter design of PLA/Wood Fused Filament Fabrication using Taguchi optimization methodology. 
[13]    Huang, Y., Löschke, S., and Proust, G., 2021. In the mix: The effect of wood composition on the 3D printability and mechanical performance of wood-plastic composites. Composites Part C: Open Access. 5. 
[14]    Chaidas, D. and Kechagias, J.D., 2022. An investigation of PLA/W parts quality fabricated by FFF. Materials and Manufacturing Processes. 37 (5), pp.582–590. 
[15]    Anerao, P., Kulkarni, A., Munde, Y., Shinde, A., and Das, O., 2023. Biochar reinforced PLA composite for fused deposition modelling (FDM): A parametric study on mechanical performance. Composites Part C: Open Access. 12. 
[16]    Morales, M.A., Atencio Martinez, C.L., Maranon, A., Hernandez, C., Michaud, V., and Porras, A., 2021. Development and characterization of rice husk and recycled polypropylene composite filaments for 3d printing. Polymers. 13 (7). 
[17]    Sekar, V., Eh Noum, S.Y., Sivanesan, S., Putra, A., Chin Vui Sheng, D.D., and Kassim, D.H., 2021. Effect of Thickness and Infill Density on Acoustic Performance of 3D Printed Panels made of Natural Fiber Reinforced Composites. Journal of Natural Fibers. 00 (00), pp.1–9. 
[18]    Vigneshwaran, K. and Venkateshwaran, N., 2019. Statistical analysis of mechanical properties of wood-PLA composites prepared via additive manufacturing. International Journal of Polymer Analysis and Characterization. 24 (7), pp.584–596. 
[19]    Sultana, J., Rahman, M.M., Wang, Y., Ahmed, A., and Xiaohu, C., 2023. Influences of 3D printing parameters on the mechanical properties of wood PLA filament: an experimental analysis by Taguchi method. Progress in Additive Manufacturing. 
[20]    Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C.C.L., Rosen, D.W., et al., 2022. Research and application of machine learning for additive manufacturing. Additive Manufacturing. 52. 
[21]    Ladani, L.J., 2021. Applications of artificial intelligence and machine learning in metal additive manufacturing. JPhys Materials. 4 (4). 
[22]    Mishra, A., Di Milano, P., and Jatti, V., 2023. Graph Neural Networks (GNN) for Tensile Strength Prediction in Additive Manufacturing. 
[23]    Mishra, A., Jatti, V.S., Sefene, E.M., and Paliwal, S., 2023. Explainable Artificial Intelligence (XAI) and Supervised Machine Learning-based Algorithms for Prediction of Surface Roughness of Additively Manufactured Polylactic Acid (PLA) Specimens. Applied Mechanics. 4 (2), pp.668–698. 
Mechanics of Advanced Composite Structures
Volume 12, Issue 2 - Serial Number 25
Special Issue on Mechanics of Advanced Fiber-Reinforced Composite Structures: Celebrating the 50th Anniversary of Semnan University, Handled by the Esteemed Journal Editor, Prof. Dr. Mavinkere Rangappa Sanjay - In Progress
August 2025
Pages 261-270

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