Segmentation and morphological analysis of wear track/particles images using machine learning

Abstract

Tribology is the science and engineering of interacting surfaces in relative motion. In this context, dry friction between two bodies generates wear particles known as third body particles. We propose to characterize these particles using image acquisition and analysis. The images of wear particles are observed by scanning electron microscopy and further segmented using machine learning at the pixel level. Thereafter, the most relevant geometrical and textural descriptors are selected by a sensitivity study and correlated to tribological characteristics. The proposed tools give first quantitative results to better understand, for industrial purposes, the mechanisms involved in the wear phenomenon, and the morphology of ejected third body particles.