Machine learning-based optimization of FFF process parameters for enhanced mechanical properties of 3D-printed PEEK in Z -direction

Abstract

Purpose Mechanical anisotropy in Z-direction printed polyether ether ketone (PEEK) components fabricated by fused filament fabrication (FFF) remains a critical limitation for load-bearing applications. Traditional response surface methodology (RSM) assumes polynomial relationships that inadequately capture the nonlinearities governing FFF of high-performance semi-crystalline polymers. This study aims to develop and validate a machine learning-based framework to optimize FFF process parameters for enhanced Z-direction mechanical properties and reduced porosity in 3D-printed PEEK. Design/methodology/approach A central composite design across 15 printing conditions provided the experimental data set. Four ML algorithms were evaluated through leave-one-out cross-validation. Feature importance was assessed via permutation importance, SHAP values and partial dependence plots. Multi-objective optimization through NSGA-II generated a Pareto front, and consensus optimal parameters were validated through ten independent tensile specimens. Findings Random forest achieved superior predictive accuracy (R² greater than 0.94, MAPE equal to 3.9%). Layer thickness was identified as the dominant parameter, with a model-predicted transition at approximately 0.22 mm where porosity increases in an accelerated manner, presented as a hypothesis requiring future experimental validation. NSGA-II generated 295 Pareto-optimal solutions. Validated parameters (408°C, 57 mm/s, 0.098 mm) yielded prediction errors below 4.1%, achieving 62% tensile strength improvement and 76% porosity reduction relative to the least favorable condition. Originality/value This work presents the first experimentally validated multi-objective ML optimization framework for Z-direction mechanical property enhancement of FFF-printed PEEK. It identifies a critical layer thickness threshold governing porosity transitions, and generates a multi-solution Pareto front enabling parameter space exploration that RSM-based approaches cannot provide for this material system.