Explainable ensemble learning with Bayesian Optimization for microstructure-driven tensile strength prediction and design optimization of Al–SiC metal matrix composites

The advancement of Al–SiC metal matrix composites (MMCs) is crucial for modern structural and aerospace applications because of their high stiffness, thermal stability, and lightweight characteristics. However, conventional optimization and experimental approaches used to achieve maximum mechanical strength are labor-intensive, expensive, and often limited by nonlinear interactions among microstructural parameters. To address these limitations, this study proposes the Metal Matrix Composite Optimization Network (MMCO-Net), a hybrid artificial intelligence-driven ensemble framework integrating Random Forest (RF) and Extreme Gradient Boosting Regression (XGBR) for accurate prediction of Ultimate Tensile Strength (UTS). The proposed MMCO-Net employs a weighted fusion mechanism that adaptively combines model contributions through cross-validation, achieving high predictive accuracy with R 2 = 0.98, RMSE = 4.6 MPa, and MAE = 3.8 MPa. Bayesian Optimization (BO) was incorporated to optimize the key microstructural parameters, namely SiC weight fraction (x 1 ) and porosity (x 2 ), in order to maximize UTS. The optimization process identified an optimal configuration of 38 wt% SiC and 1.6% porosity, resulting in a predicted peak UTS of 326.4 MPa. Furthermore, SHAP-based explainability analysis revealed that SiC weight fraction contributed most positively toward tensile strength, whereas porosity exhibited the strongest negative influence. Comparative analysis demonstrated that MMCO-Net outperformed conventional Random Forest, Linear Regression, and CatBoost models in prediction accuracy and stability. The proposed MMCO-Net framework significantly reduces dependence on exhaustive experimental iterations and establishes a strong foundation for future multi-property prediction and intelligent composite material engineering.