Lessons learned using machine learning to link third body particles morphology to interface rheology

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