Fused Deposition Modeling (FDM) is a widely used additive manufacturing technology for polymer components due to its low cost, ease of processing, and material versatility. However, FDM-produced parts exhibit pronounced anisotropy in mechanical properties owing to interlayer adhesion variations, porosity, and thermal gradients. Polylactic acid (PLA), a biodegradable thermoplastic, is commonly employed in FDM applications, and its tensile strength is highly sensitive to printing parameters. In this study, the effects of printing speed (PS, 100–125–150 mm/s), occupancy (O, 50–75–100%), layer height (LH, 0.2–0.25–0.3 mm), wall thickness (WT, 0.8–1.2–1.6 mm), and nozzle temperature (NT, 175–200–225°C) on the tensile strength of PLA were systematically investigated. To efficiently explore this multi-parameter space, a Taguchi L27 experimental design was implemented, and the contribution and statistical significance of each factor were evaluated via ANOVA. Furthermore, machine-learning predictions were generated using linear and polynomial regression; ensemble tree-based methods (Random Forest and gradient boosting, e.g., XGBoost); artificial neural networks (MLP); kernel-based methods (SVR); instance-based methods (KNN); Gaussian Process Regression (GPR); and hybrid stacking models, all validated through cross-validation. The results of the ANOVA indicated that wall thickness was the most influential factor on tensile strength, contributing 58% of the variance, followed by printing speed, layer height, and infill density, while nozzle temperature, despite being statistically significant, had a limited practical effect. The optimal printing conditions, determined experimentally, were PS = 125 mm/s, O = 50%, LH = 0.3 mm, WT = 1.6 mm, and NT = 225°C. Notably, ensemble and neural network models, such as MLP, XGBoost, and gradient boosting achieved R 2 values above 0.99, demonstrating high predictive accuracy and effectively capturing the complex relationships between process parameters and mechanical performance. This study provides a reliable and effective framework for optimizing the mechanical properties of FDM-printed PLA components and for advanced machine-learning-based predictions, thereby making a significant contribution to the additive manufacturing literature.
Mehmet Sah Gultekin (Thu,) studied this question.