Non-contact Surface Roughness Measurement of Engineering surface by the application of digital image magnification

N. BOSE, D. Palani Kumar

Abstract


Computer-integrated manufacturing requires fast and accurate systems that provide the feedback to control the machining process and improve product quality and productivity. On-line process monitoring has been an active area of research because it is recognized as an essential part of fully automated manufacturing systems. One of the parameters to be controlled in machining is surface finish, which is a vital criterion in the performance and utility of industrial products. The computer vision based system is used to analyze the pattern of scattered light from the surface to assess the surface roughness of the component. Extensive research has been performed on machine vision applications in manufacturing, because it has the advantage of being non-contact and as well faster than the contact methods. Unlike the stylus instruments, the computer vision systems have the advantages of being non-contact and are capable of measuring an area of the surface rather than a single line which makes it a 3D evaluation. In this research work, a machine vision system has been utilized to capture the images of ground surfaces and then the quantification of digital pictures of ground surfaces is done. Subsequently, original images of ground surfaces have been magnified using Cubic Convolution, Nearest Neighbor and Bilinear interpolation techniques. Then the optical surface roughness parameter Ga has been estimated for all the captured surface images and for the magnified quality improved images. Finally, a comparison has been carried to establish correlation between magnification index and surface roughness.

Keywords—Cubic convolution, Magnification factor, Bilinear interpolation, Surface roughness.

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References


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