Explainable AI analysis of printing parameter effects on life cycle assessment for sustainable material-extrusion additive manufacturing

As material extrusion additive manufacturing (MEX) moves into industrial use, its electricity and filament consumption translate into non-negligible life cycle assessment (LCA) impacts. We propose a unified explainable AI (XAI)–LCA framework that links four printing parameters (print speed, layer height, infill density, raster angle) to four ReCiPe 2016 Midpoint (H) indicators at the printing stage. Slicer-generated datasets are combined with random forest models, Shapley additive explanations (SHAP), response surfaces and partial dependence plots (PDPs) to quantify how parameters affect impacts through energy- and material-driven pathways. The resulting decomposition model achieves coefficients of determination R2>0.97 and mean absolute percentage errors (MAPE) below 7% in ten repeated splits; blind validation on fifteen unseen configurations yields MAPE