Using Machine Learning to Predict the Wear Rate of Corundum‐Filled LM30 Aluminum Composites at High Temperatures

This study emphasizes the use of machine learning (ML) to optimize the design of a cost‐effective, high‐temperature, wear‐resistant composite brake rotor material. Corundum‐reinforced LM30 aluminum alloy composites were fabricated with varying particle sizes (1–20, 32–50, and 75–106 μm) and weight fractions (5–20 wt.%). Instead of relying only on experimental trials, ML models were used to capture the complex relationship between these input variables and the resulting wear properties. Among the tested models, the support vector machine with radial basis function (SVM_RBF) showed excellent predictive ability, with a correlation coefficient (CC) of 0.992, mean absolute error (MAE) of 1.666, and root mean square error (RMSE) of 2.737. The predictions revealed that smaller particle sizes and higher weight fractions significantly improve wear resistance. By leveraging ML, this study demonstrates how predictive tools can guide material design, reduce experimental cost, and accelerate the development of durable and high‐performance composites.