Prediction of tensile strength of PLA parts produced with FDM using Taguchi L27, ANOVA, and large-scale machine learning models and optimal parameter design

Abstract

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 R2 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. © The Author(s) 2026