The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms. Data-driven methods, including machine learning (ML) algorithms, can yield a better comprehensive understanding of complex problems under the influence of multiple parameters, typically for how tribological performances and material properties correlate. Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data. Herein, the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates, with R² of 0.9219 and 0.9243, respectively. Feature importance analysis also revealed that the content of graphite and the hardness of the matrix have the greatest influence on the friction coefficients, and the normal load, the content of graphite, and the hardness of the matrix influence the wear rates the most.

自润滑复合材料的摩擦学性能受到许多变量和复杂机制的影响。包括机器学习 (ML) 算法在内的数据驱动方法可以更好地全面理解多个参数影响下的复杂问题，通常是摩擦学性能和材料特性之间的关联。铜/铝-石墨（Cu/Al-graphite）自润滑复合材料的摩擦系数和磨损率与其固有材料性能（成分、润滑剂含量、颗粒尺寸、加工工艺和界面结合强度）以及相关变量的相关性使用传统方法对测试方法（法向载荷、滑动速度和滑动距离）进行分析，然后通过五种不同的ML算法（即支持向量机（SVM）、K近邻（KNN））对摩擦学特性进行建模和预测、随机森林 (RF)、极限梯度提升 (XGBoost) 和最小二乘提升 (LSBoost)，基于摩擦学实验数据。结果表明，机器学习模型可以根据材料特性和测试方法变量数据令人满意地预测摩擦系数和磨损率。其中，基于集成学习算法的LSBoost模型对摩擦系数和磨损率的预测性能最好，R ²分别为0.9219和0.9243。特征重要性分析还表明，石墨含量和基体硬度对摩擦系数影响最大，法向载荷、石墨含量和基体硬度对磨损率影响最大。