Understanding the Role of Glaze Layer With Multiple Surface Characterization Techniques Aligned by Computer Vision Algorithms

Abstract

A glaze layer that significantly reduces friction and wear has been found on the surface of many Fe-, Cr-, and Ni-based material systems undergoing fretting/sliding at elevated temperature. In this work we proposed a novel way to understand the role of glaze layer using computer vision algorithms. Two workflows, one for quantitative glaze layer identification and the other for image alignment, have been developed. For glaze layer identification, we used computer vision concepts that considers the color and reflectiveness of glaze layer under optical microscope (OM). For image alignment, we developed a strategy to conduct pixel-to-pixel alignment of images acquired by multiple techniques (e.g., OM, scanning electron microscopy, 3D optical profilers) with sub-pixel error. As such, the correlation between the height map and locations of the glaze layer within the wear scar can be readily determined. These methods are used to evaluate wear scars and quantify glaze layer coverage on 310S stainless steel under like-on-like, cylinder-on-flat fretting conditions from 20°C to 700°C. The glaze layer is found to always occupy relatively high locations within wear scar, and the height difference between glaze layer and none-glaze layer is statistically significant. The results provide evidence that severe-to-mild wear transition resulted from spreading of glaze layer coverage, and glaze layer may reduce friction and wear by reducing real contact area. The open-source workflows we developed are powerful tools that enable multi-spectrum analysis without upgrading existing characterization tools, which can be easily transferable to all other applications in academia and industry.