The optimization of functionally graded metal matrix composites (FGMMCs) has long been hindered by the complex interplay between reinforcement distribution, process constraints, and multifunctional performance objectives. Current data-driven and empirical frameworks like ANN-GA hybrids, CNN-DQN models and XGBoost regressors cannot guarantee physical validity, as well as generalizability, and, as a result, provide non-manufacturable or unstable predictions. To overcome these shortcomings, this study presents a Physics-Informed Neural Operator (PINO)-assisted inverse design workflow of the Al6061 hybrid composites that combines Principal Component Analysis (PCA) to reduce dimensionality of the gradient fields and Bayesian Optimization (BO) to optimize the performance of the material-synthesizing system.
