Data-Driven Machine Learning for Uncertainty-Aware Strength Modeling in FDM Additive Manufacturing: A Probabilistic Framework for Reliability-Based Process Optimization of PETG Parts

Fused deposition modeling (FDM) parts show condition-dependent variability in ultimate tensile strength (UTS), yet most machine learning (ML) studies report only deterministic point estimates, limiting use in safety-critical design. This work presents a probabilistic framework mapping FDM-PETG process parameters to orientation-specific conditional quantile estimates with pooled uncertainty, covering model selection, deployment, and process optimization. A controlled experiment varied 12 parameters across 111 printing conditions from a combined Taguchi and Latin Hypercube Sampling (LHS) design, yielding 333 ASTM D3039 tensile measurements. Six probabilistic models were trained on raw replicate data and ranked on point accuracy and calibration, weighted 75% toward uncertainty quantification (UQ). The mixture density network (MDN) showed the lowest composite rank. Orientation-specific (MDN-OR) and unified (MDN-UF) variants were compared; MDN-OR gave more conservative lower-quantile estimates for safety-critical design. Lower-quantile (q = 0.05) SHAP analysis identified filament consumption and wall loops as the strongest predictive associations with worst-case strength. For each orientation, the configuration with minimum filament consumption at maximum strength was selected, with its post hoc q = 0.05 lower bound confirmed by physical tensile testing. Within the studied single-material, single-printer setting, the framework shows how probabilistic process–property modeling can support uncertainty-aware parameter selection toward autonomous additive manufacturing.