Machine-Learning-Enabled Tensile Property Prediction of Fused-Filament-Fabrication-Printed Recycled PLA/Wood Composites Fabricated via Solution Casting

V.V.D. Sahithi, Dhanunjay Kumar Ammisetti, Kruthiventi Sai Sarath, Priyaranjan Samal, Ravi Kumar Kottala, Seepana Praveenkumar, Jamal Eldin F. M. Ibrahim

This study investigated the manufacturing and impact of critical input parameters in fused filament fabrication (FFF) on the ultimate tensile strength (UTS) of tailor-made recycled PLA/wood bio-composite specimens. As technology advances rapidly, several wood-based polymer composites have emerged as promising materials for wood-based interior applications. In the current work, recycled PLA material is combined with wood powders to form composite 3D printing filaments. The solution casting method is used to recycle the PLA and a single-screw extruder is used to fabricate the composite filament. 3D printing parameters play a major role in enhancing the characteristics of the wood-based polymers. This study considers the printing temperature (PT), layer height (LH), and printing speed (PS) as input parameters at five levels. Taguchi Design of Experiments (L25 orthogonal array) was employed to minimize experimental runs, followed by ANOVA analysis to find influencing factors. The results demonstrated that layer height (83.91% contribution) is the most critical parameter, with 0.1 mm identified as the optimal amount for achieving the maximum UTS response, while printing temperature (2.47%) had a moderate effect and printing speed (2.28%) showed negligible influence. In the present work, the tensile properties of the composite filament were predicted using machine learning methodologies, including random forest (RF), support vector regressor (SVR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XG Boost), and Adaptive Boosting (Adaboost). The results indicate that support vector regressor (SVR) outperformed all other models in terms of generalization, as it generated the lowest test errors (mean squared error (MSE) = 0.0169, mean absolute error (MAE) = 0.0953, mean squared logarithmic error (MSLE) = 0.0065 and mean absolute percentage error (MAPE) = 0.2246) and the highest predictive power (coefficient of determination (R2) = 0.8679).