We study wear and friction of dry and lubricated aluminum-graphite composites and the transition between lubrication regimes. Using Principal Component Analysis, we perform dimensionality reduction for the 14 material and tribological variables to find clusters in friction and wear data. Five standalone and one hybrid supervised regression models were developed to predict friction and wear of lubricated composites. ML analysis identifies lubrication condition and lubricant viscosity as the most important variables. Unlike dry, graphite content has a reduced impact on the tribological behavior with liquid lubricants. The incorporation of graphite in the matrix of aluminum alloys enables them to run under boundary lubrication and run for more extended periods with lower friction even after the lubricant is drained out.

我们研究了干燥和润滑的铝-石墨复合材料的磨损和摩擦以及润滑状态之间的过渡。使用主成分分析，我们对 14 个材料和摩擦学变量进行降维，以找到摩擦和磨损数据中的集群。开发了五个独立的和一个混合监督回归模型来预测润滑复合材料的摩擦和磨损。ML 分析将润滑条件和润滑剂粘度确定为最重要的变量。与干燥不同，石墨含量对液体润滑剂的摩擦学行为的影响较小。铝合金基体中的石墨使它们能够在边界润滑下运行，即使在润滑剂排出后，也能以更低的摩擦运行更长的时间。