Automatic diagnosis and thickness determination for white etching layers in deep drilled steels based on thresholding and machine learning algorithms

The reliable detection and precise assessment of white etching layers (WEL) are key challenges in the investigation of a component’s surface integrity. This paper proposes an innovative methodology for evaluating the extent of WEL in quenched and tempered steels, machined by Boring and Trepanning Association (BTA) deep hole drilling. Micrographs obtained by light microscopy were partitioned into classes by three methods, separating the WEL from the base material and the embedding resin. Traditional manual segmentation was performed as a benchmark for automatic segmentation methods. A gray level thresholding-based method served for the segmentation of micrographs partitioned into subsets. In addition to conventional manual and thresholding-based segmentation, a machine learning-based approach for image segmentation was applied. The segmented images were further analyzed by a newly developed set of algorithms, implemented to obtain detailed information on the WEL, e.g. their average thickness as well as the area covered by WEL in the micrographs. Results indicate that both, gray level thresholding, as well as machine learning-based image segmentation, show potential for the automated diagnosis and assessment of WEL. They both yield quantitatively similar, but less biased results compared to manual segmentation.