Multi-parameter optimization of FDM printed part dimensional accuracy based on response surface methodology

Abstract This study employs Response Surface Methodology (RSM) for the multi-parameter optimization of Fused Deposition Modeling (FDM) process parameters to enhance the dimensional accuracy of polylactic acid (PLA) printed parts. A Box-Behnken Design (BBD) was utilized to construct a predictive model correlating key process parameters: layer height, speed, substrate temperature, and nozzle temperature, with dimensional errors. Analysis of variance (ANOVA) and parameter optimization were performed using De-Sign-Expert software, elucidating the interaction effects among parameters. Results demonstrate a significant improvement in dimensional accuracy under the optimized parameter set: a layer height of 0.14 mm, print speed of 42 mm/s, substrate temperature of 44°C, and print temperature of 214°C. The established RSM model accurately predicts dimensional errors within a 95% confidence level, validated through confirmatory experiments. This model provides an efficient optimization framework for the precision manufacturing of PLA via FDM, offering substantial value for advancing low-cost, high-accuracy 3D printing applications.