Machine Learning-Based Prediction of the Dry Sliding Wear Behaviour of Al2O3-Al6061 Metal Matrix Composites

This study investigated the dry sliding wear behaviour of Al2O3 particulates (2–6 wt.%) in Al6061 metal matrix composites produced by ultrasonic stir casting, examining mechanical and tribological behaviour. We modelled wear rate using machine learning. Optical studies confirmed uniform Al2O3 particle dispersion, minimal agglomeration, and strong interfacial bonding between the Al6061 and Al2O3 phases. With an increase in Al2O3 content, density and hardness increased by 1.3% and 33%, respectively. The Al6061–6 wt.% Al2O3 composite exhibited 40% higher wear resistance than the base alloy in dry-sliding conditions, with sliding distance varying between 0 and 10,000 m and load varying between 0 and 50 N. At lower loads and shorter sliding distances, abrasive wear dominated; as load and sliding distance increased, the dominant wear mechanism shifted to delamination and adhesion wear. Moreover, tribological testing at 6 wt.% reinforcement showed a 40% improvement in dry sliding wear resistance, with applied normal load and sliding distance varying between 10 and 50 N, and 1000 and 10,000 m, respectively. The specific wear rate was subsequently modelled and predicted using K-Nearest Neighbours (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANNs), Random Forests (RFs), and Gradient Boosting Machines (GBMs). Among these, the RF model achieved the highest accuracy (R2 = 0.946). Based on feature importance analysis, applied normal load and sliding distance are the most influential factors in wear. As a result, in dry-sliding conditions, Al6061-Al2O3 MMCs show a stable wear response, thereby improving dataset homogeneity and model performance. Overall, this study used experimental insights and predictive analytics to predict MMC wear behaviour. By employing ML models, composite design and wear can be optimised. Additionally, feature importance analysis showed that applied normal load and sliding distance best predicted wear rate.