Mechanical strength of PLA parts manufactured by FDM using RSM and fuzzy logic

Abstract

In 3D printing, the mechanical properties of the products can be improved by optimizing manufacturing parameters. In this study, the manufacturing parameters that influence the mechanical performance of PLA (polylactic acid) components produced using fused deposition modeling (FDM) were investigated through experimental testing and modeling-based optimization techniques. Three key manufacturing parameters (raster angle, infill type, and infill rate) were selected to evaluate their effects on ultimate tensile strength (UTS) and impact strength (IS), which were used as performance criteria. An L18 orthogonal array was used for the experimental design, and tensile and impact tests were conducted in accordance with ASTM standards. Two different optimization techniques were used to predict mechanical properties: Response surface methodology (RSM) and fuzzy logic (FL). A comparative evaluation based on experimental data revealed that RSM provides superior prediction accuracy, with average error rates of 3.89 % for UTS and 9.57 % for IS, while FL exhibits higher deviations (6.35 % and 13.35 %, respectively). The results highlight the importance of statistical modeling in predicting mechanical behavior in additive manufacturing processes and demonstrate that RSM produces more reliable results than FL for parameter optimization. This study provides practical insights for engineers aiming to improve the mechanical performance of 3D printed components.