Machine Learning Prediction and Experimental Validation of Microhardness in High-Entropy Alloy Reinforced Aluminium Composite

Abstract This study introduces a novel approach for the prediction and experimental validation of microhardness in High Entropy Alloy (HEA) reinforced Aluminium composites using advanced Machine Learning (ML) techniques. For the first time, five ML models, including XGBoost, Stacking Regressor, Quantile Random Forest, Gaussian Process, and Sparse Polynomial Regression were employed to predict the microhardness of Al/HEA composites. The models were trained using an 80:20 train-test split, with hyperparameters optimized via random search and cross-validation. Among the models, XGBoost delivered the highest accuracy, with an R² value of 0.91, and predicted a microhardness of 177 HV. To validate these predictions experimentally, LM13 aluminium alloy was selected as the matrix material, reinforced with HEA (AlTiVZrCrMo) synthesized through stir casting route. Scanning Electron Microscopy (SEM) analysis confirmed uniform reinforcement distribution, contributing to an experimental microhardness of 168 HV, a 45% improvement over the base alloy. The minimal 5.8% deviation between predicted and experimental values demonstrates the effectiveness of ML in predicting material properties, significantly reducing the need for extensive experimental work.