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

Tomasz Bartkowiak,Bartosz Gapiński,Karol Grochalski,Michał Wieczorowski

doi:10.3390/ma14227044

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.

Highlights

The objective of this paper is to demonstrate the use of three different multiscale methods to discriminate between topographies that were created by two stage formation: hot rolling and mass finishing
They focused on standard height parameters calculated as a function of evaluation length and the degree of polynomial fitted for roughness data
This study addresses the last problem by comparing different multiscale characterizations applied to discriminate topographies created by a combination of hot-rolling and masslafsint ipsrhoinbgle.mAbs yacroemsupltaroinf gmdainffuefraecnttumrinugltipsrcoacleescsheasr,atcetxetruizraetsioonfsaappppalrieendttsoimdiislcarimtieisnaatned todpisosgimraiplahriietisecsraeraeteodbtbayinaedcowmhbicinhaatrieondioscfehronti-brloellaitnagcaenrtdaimn ascssa-lfie noirsshcianlge.s Aofsoabsrersvualtiofn. manufacturing processes, textures of apparent similarities and dissimilarities are obtained w2h. iMchaatereridaliscaenrndibMleetahtoadcsertain scale or scales of observation

Summary

Introduction

The objective of this paper is to demonstrate the use of three different multiscale methods to discriminate between topographies that were created by two stage formation: hot rolling and mass finishing. From the quality control perspective, it is essential to be able to differentiate or distinguish between surfaces that perform and were fabricated, modified, or treated differently This ability is called discrimination, and thanks to multiscale analysis, it becomes possible to identify what surface characterization techniques, relating parameters, and scales are the most convenient at discerning topographies. Bigerelle et al [5] studied the ability to discriminate the surface roughness of plastic parts created by injection molding, concentrating on four processing parameters In that research, they focused on standard height parameters calculated as a function of evaluation length and the degree of polynomial fitted for roughness data. This study aims at comparing the performance of each multiscale method in the process of discriminating between measured samples This is done based on characterization parameters, calculated using each multiscale methods, at each scale available in the measurement using two-way ANOVA.

Length- and Area-Scale Analysis

Curvature

Findings

Conclusions