Abstract This paper reports a preliminary investigation on the ability of Machine Learning algorithms to relate the morphology of third body particles to the rheology of the contact interface that created them. A testing campaign is performed on a pin-on-disc tribometer, followed by a comprehensive observation of the worn surfaces. Several Machine Learning algorithms are then used to establish and quantify the logical relations between the rheological and the morphological databases built from this campaign. Success rates and thorough analysis of their predictions are used to validate the general approach and to propose possible improvements. It appears that Machine Learning presents an interesting potential in quantitative tribological analysis if the morphological and rheological databases are properly enriched.

中文翻译：

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使用机器学习将第三体粒子形态与界面流变学联系起来的经验教训

摘要 本文报告了机器学习算法将第三体粒子的形态与产生它们的接触界面的流变学联系起来的能力的初步调查。在销盘式摩擦计上进行测试活动，然后对磨损表面进行全面观察。然后使用几种机器学习算法来建立和量化从该活动建立的流变学和形态学数据库之间的逻辑关系。成功率和对其预测的彻底分析用于验证一般方法并提出可能的改进方案。如果形态学和流变学数据库得到适当的丰富，机器学习似乎在定量摩擦学分析中呈现出有趣的潜力。