Discrimination of Surface Topographies Created by Two-Stage Process by Means of Multiscale Analysis

Abstract

The fundamental issue in surface metrology is to provide methods that can allow the establishment of correlations between measured topographies and performance or processes, or that can discriminate confidently topographies that are processed or performed differently. This article presents a set of topographies from two-staged processed steel rings, measured with a 3D contact profilometer. Data were captured individually from four different regions, namely the top, bottom, inner, and outer surfaces. The rings were manufactured by drop forging and hot rolling. Final surface texture was achieved by mass finishing with spherical ceramic media or cut wire. In this study, we compared four different multiscale methods: sliding bandpass filtering, three geometric length- and area-scale analyses, and the multiscale curvature tensor approach. In the first method, ISO standard parameters were evaluated as a function of the central wavelength and bandwidth for measured textures. In the second and third method, complexity and relative length and area were utilized. In the last, multiscale curvature tensor statistics were calculated for a range of scales from the original sampling interval to its forty-five times multiplication. These characterization parameters were then utilized to determine how confident we can discriminate (through F-test) topographies between regions of the same specimen and between topographies resulting from processing with various technological parameters. Characterization methods that focus on the geometrical properties of topographic features allowed for discrimination at the finest scales only. Bandpass filtration and basic height parameters Sa and Sq proved to confidently discriminate against all factors at all three considered bandwidths.