Computational modeling and interpretability analysis of manufacturing parameters in titanium matrix composites

The design of high-performance titanium matrix composites (TMCs) often faces the challenge of complex, nonlinear interactions between multi-scale processing parameters and mechanical properties. To overcome the limitations of traditional empirical methods, this study establishes a comprehensive data-driven computational framework to predict and optimize the performance of TMCs. To address the challenge of small-sample generalization, a diversity-oriented independent validation strategy was employed, strictly separating model training from a heterogeneous hold-out test set. Among various evaluated algorithms, the gradient boosting (GB) model demonstrated superior fidelity, achieving high predictive accuracy on the unseen independent datasets (R² up to 0.88 for strength). To ensure model transparency, a synergistic interpretability framework was implemented, integrating Pearson correlation, feature importance screening, SHapley Additive exPlanations (SHAP), and individual conditional expectation (ICE) plots. This multi-level approach quantitatively identified reinforcement characteristics, hot rolling reduction, and sintering pressure as the primary mechanical descriptors. Crucially, the model captured distinct, non-linear optimization regimes, identifying optimal property windows at lower pressures (40–50 MPa) for spark plasma sintering (SPS) and higher pressures (300–500 MPa) for hot-pressing, both in synergy with 70–75% rolling reductions. Experimental validation on a 2.0 wt% MXene/Ti composite confirmed the model's high-fidelity strength prediction with errors <2%. This ML-driven strategy provides a computationally efficient paradigm for defining tailored processing envelopes, offering new insights into the inverse design of high-performance metal matrix composites.