Experimental and machine learning-based evaluation of mechanical performance in FDM-printed laminated polymer structures
This study presents an experimental and machine learning (ML) based investigation of the mechanical performance of laminated polymer structures fabricated by fused deposition modeling (FDM). The specimens were designed as a three-layer configuration consisting of polyethylene terephthalate glycol (PETG), thermoplastic polyurethane (TPU), and polylactic acid (PLA) to combine rigidity, elasticity, and dimensional stability. Mechanical performance was evaluated through tensile and flexural tests under different processing conditions. A Taguchi L16 orthogonal array was employed to investigate the effects of four printing parameters: nozzle temperature (NT), infill pattern (IP), wall thickness (WT), and printing speed (PS), each at four levels. Experimental results were analyzed using signal to noise (S/N) ratios and analysis of variance (ANOVA). Tensile strength ranged from 23.19 to 36.22 MPa, while flexural strength varied between 10.29 and 53.40 MPa. ANOVA revealed that WT was the most influential factor affecting tensile strength ( p = 0.003), whereas NT had the greatest effect on flexural strength ( p = 0.029). To enhance predictive capability, four ML algorithms were developed and compared. These were artificial neural network (ANN), support vector regression (SVR), random forest (RF), and extra trees (ET). The ET model achieved the highest predictive accuracy, with R 2 values of 0.875 for tensile strength and 0.906 for flexural strength. The results demonstrate that ensemble tree-based models capture nonlinear relationships between FDM parameters and mechanical responses more effectively than ANN and SVR. The integration of Taguchi design, statistical analysis, and ML provides an effective framework for predicting and optimizing the mechanical performance of FDM-printed PETG/TPU/PLA laminated polymer structures.