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Proceedings ArticleDOI

Surface roughness evaluation using machine vision approach and Hurst operator

29 Jul 2002-Vol. 4900, pp 768-776
TL;DR: In this paper, a mean value mask is applied over the image a number of times to generate the reference intensity surface, which is then subtracted from the original image to reveal the surface roughness information, and the Hurst operator is then applied over this intensity information to estimate the optical roughness parameter.
Abstract: It has been proved by researchers that computer vision has real potential when applied to the automated measurement of surface roughness of engineering components. The present study considers the detailed examination of surface roughness using 2D-image information. First the image is smoothed out using a conventional low pass filter to filter the roughness information from other erroneous information accounting to lighting, waviness, from errors and other effects. A mean value mask is applied over the image a number of times to generate the reference intensity surface. This reference intensity surface is then subtracted from the original image to reveal the surface roughness information. The Hurst operator is then applied over this intensity information to estimate the optical roughness parameter. A correlation graph has to be plotted to relate this optical roughness value with the value (Ra) obtained from the stylus profilometer. In this procedure the workpieces used were of mild steel and they were machined using different processes like milling, shaping and grinding to ensure the repeatability of results. In this process the orientation of workpiece doesn't effect the process and the lighting effect is not considered, and is maintained and assumed that it is identical for all the images.
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Journal ArticleDOI
TL;DR: In this paper, a microcomputer-based vision system was used to analyze the pattern of scattered light from the surface to derive a roughness parameter, and a correlation curve was established by plotting the roughness parameters against the corresponding average surface roughness readings obtained from a stylus instrument.
Abstract: A new method of surface roughness measurement was developed for use in a production environment. This method employs a microcomputer-based vision system to analyse the pattern of scattered light from the surface to derive a roughness parameter. The roughness parameters were obtained for a number of tool-steel samples which were ground to different roughnesses. A correlation curve was established by plotting the roughness parameters against the corresponding average surface roughness readings obtained from a stylus instrument. Similar correlation curves were produced for different materials such as brass and copper. Surface roughness measurement was also performed for specimens immersed in oil, a condition similar to that of a production environment. Some observable trends were found. The proposed method provides a fast and accurate means for measuring surface roughness. Its repeatability and versatility compares favourably with other methods.

100 citations