Machine Learning-Based Estimation of Mechanical Properties of 3D-Printed PLA Composites Under Annealing Treatment

This study presents a novel, data-driven framework for predicting and optimizing the mechanical performance of 3D-printed polylactic acid (PLA) composites reinforced with date pit (DP) particles under controlled annealing conditions. This work uniquely integrates both effects into a unified machine learning (ML) surrogate modeling approach, enabling simultaneous property prediction and design optimization from a minimal experimental dataset. Three ML algorithms, Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree Regression (DTR), were systematically developed to predict Young's modulus (E), ultimate compressive strength (UCS), and Shore D hardness as functions of DP weight fraction (0-10 wt.%) and annealing duration (0-20 h at 100 °C). Among the models, GPR demonstrated superior generalization and uncertainty-aware prediction capability, achieving test R² values up to 0.936 and a MAPE below 2% for structurally critical properties. Beyond prediction accuracy, the study introduces a key novelty through the integration of Partial Dependence Plot (PDP) analysis, which reveals physically interpretable relationships between processing parameters and mechanical behavior. A non-monotonic annealing optimum at 5 h was identified, governed by competing crystallization enhancement and thermal degradation mechanisms, alongside a consistently positive reinforcement effect of DP loading. The optimized condition (10 wt.% DP, 5 h annealing) yielded improvements of 37.5% in stiffness, 42.4% in strength, and 21.6% in hardness compared to neat PLA. A five-fold cross-validation and split ratio sensitivity analyses (70/30, 80/20, 90/10) were conducted to assess generalization reliability. One-way ANOVA confirmed highly significant effects of DP loading on all three properties (p < 0.001 for E and hardness; p < 0.01 for UCS) and a significant effect of annealing time on UCS (p < 0.01). Permutation feature importance analysis confirmed DP weight fraction as the dominant predictor for all three outputs.