Data-driven methods including machine learning (ML) algorithms can yield a better understanding of how tribological and material properties correlate. Correlations of friction and wear of aluminum (Al) base alloys with their material properties (hardness, yield strength, tensile strength, ductility, silicon carbide content), processing procedure, heat treatment, and tribological test variables (sliding speed, sliding distance, and normal load) studied using traditional and data-driven approaches. Five different ML algorithms, K Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Gradient Boosting Machine (GBM) applied to experimental tribological data to predict the coefficients of friction (COF) and wear rates. Through performance analysis, we demonstrated that the ML models can satisfactorily predict friction and wear of Al alloys from material and tribological test variables data. Comparative analysis of model performance illustrated that RF outperformed other ML models in wear rate prediction, while KNN exhibited the best performance in COF prediction. Feature importance analysis further revealed that normal load, hardness, and sliding speed have the maximum influence in predicting the wear rate of the alloys. The variation in hardness of the alloys and sliding distance influenced the COF prediction the most.

包括机器学习（ML）算法在内的数据驱动方法可以更好地了解摩擦学和材料特性之间的关系。铝（Al）基合金的摩擦磨损与它们的材料特性（硬度，屈服强度，拉伸强度，塑性，碳化硅含量），加工过程，热处理和摩擦学测试变量（滑动速度，滑动距离和正常负载）使用传统方法和数据驱动方法进行了研究。五个不同的ML算法，K最近邻（KNN），支持向量机（SVM），人工神经网络（ANN），随机森林（RF）和梯度增强机（GBM）应用于实验性摩擦学数据以预测摩擦系数（COF）和磨损率。通过性能分析，我们证明了ML模型可以从材料和摩擦学测试变量数据中令人满意地预测铝合金的摩擦和磨损。对模型性能的比较分析表明，在磨损率预测中，RF优于其他ML模型，而KNN在COF预测中表现出最好的性能。特征重要性分析进一步表明，法向载荷，硬度和滑动速度对预测合金的磨损率具有最大的影响。合金硬度和滑动距离的变化对COF的预测影响最大。特征重要性分析进一步表明，法向载荷，硬度和滑动速度对预测合金的磨损率具有最大的影响。合金硬度和滑动距离的变化对COF的预测影响最大。特征重要性分析进一步表明，法向载荷，硬度和滑动速度对预测合金的磨损率具有最大的影响。合金硬度和滑动距离的变化对COF的预测影响最大。