Machine learning-based prediction of coefficient of friction and specific wear rate in aluminum-based metal matrix composites

Abstract

This study introduces a machine learning (ML) framework to predict the Coefficient of Friction (CoF) and Specific Wear Rate (SWR) in aluminum-based composites reinforced with SiC and MoS₂. Utilizing a robust dataset of 948 records, eight ML models were developed and optimized via GridSearchCV. Hyperparameter tuning was transformative, with the optimized Ridge model achieving exceptional test R² values of 0.96 for CoF and 0.90 for SWR. Feature importance analysis identified Material, Sliding Load, and MoS₂% as critical factors. The models infer a key synergistic mechanism: SiC enhances wear resistance, while MoS₂ reduces friction. This work validates a finely-tuned ML approach as a highly accurate and efficient computational alternative to traditional experimental methods for tribological performance modeling and MMC design.