Tribological Analysis of A356 composites using Machine learning algorithms

Abstract This study examines the wear characteristics of A356-Al 2 O 3 -B 4 C composites produced via stir casting, incorporating three reinforcement compositions (5, 10, and 15 wt.% Al 2 O 3 with a constant 5 wt.% B 4 C). We used seven different machine learning algorithms to predict wear rates based on experimental tribological data: Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Light GBM, and AdaBoost. The Random Forest model was better at making predictions, with a R 2 of 0.83, a mean absolute error (MAE) of 0.0012 m 3 /Nm, a mean absolute percentage error (MAPE) of 1.3%, and a root mean squared error (RMSE) of 0.0094 m 3 /Nm, which means it was 83% accurate. Experimental wear rates ranged from 4.3 × 10 −14 to 3.43 × 10 −13 m 3 /Nm across varying sliding distances (1500–6000 m), loads (10–20 N), and velocities (1.45–2.66 m/s). Systematic k-fold cross-validation and grid search hyperparameter Optimisation to make sure the model worked well. The results show that machine learning, especially ensemble tree-based methods, can accurately predict how well composite materials will wear, which speeds up the process of optimizing material design and cuts down on the cost of experiments. This study shows that combining artificial intelligence with traditional tribological characterization can improve the ability to predict how composite materials will behave in the future. It also sheds light on the relative importance of tribological operating parameters.