Investigation of Correlation between Image Features of Machined Surface and Surface Roughness

Abstract

Alternative approach to surface roughness evaluation is mostly based on the analysis of digital images of machined surfaces i.e. on extracting various features from the matrix mathematically representing a digital image. This paper analyses correlation between 23 different digital image features and the surface roughness for two different materials: aluminium and stainless steel. Machined surfaces for both materials were acquired by face milling. Factorial design 6 × 5 × 2 with two replicates was conducted for each material with cutting parameters being varied on various numbers of levels. Based on the correlation coefficients the results showed that the best ranked features regardless of the machined material were the features based on statistic measures.

Micro- and Nano-Roughness Separation Based on Fractal Analysis

“…In roughness measurements, RC and Gaussian filters have traditionally been used to separate roughness and waviness. To meet growing demands, the enhanced dual-Gaussian filter and the robust or spline filters are available for industry and researchers [4][5][6]. The evaluation technique based on motif combinations, where there are also ways of separating roughness and waviness, has had long traditions [7].…”

Micro- and Nano-Roughness Separation Based on Fractal Analysis

“…In roughness measurements, RC and Gauss filters have traditionally been used to separate roughness and waviness. To meet growing demands, the enhanced dual-Gauss filter, the robust or the spline filters are available for industry and researchers [4][5][6]. The evaluation technique based on Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”