Non-contact surface roughness measurement using laser speckle technique

Abstract

This paper deals with selected contact type stylus method and non-contact type machine vision method using laser speckle for components prepared by grinding of AISI 1040 steel with a variety of wheels and varied depth of cut. In this interactive study, Optical method based on statistical properties of binary images is proposed for machined surfaces. Grounded metal surfaces are used to develop a binary digitized speckle pattern by a beam of He-Ne laser light source. High end camera is used to capture the image of a speckle pattern. White to black pixels ratios is computed from the binary images using image processing toolbox in Matlab. The correlation is developed between white to black pixels ratio and measured two-dimensional surface roughness parameter. Two-dimensional surface roughness parameters are recorded using a contact-type surface profilometer. The results which opted, clearly supports that these parameters have a relationship with a degree of surface roughness. A linear relationship is observed between parameter obtained from proposed technique and measured value of surface roughness using surface profilometer. The statistical analysis represents the performance of maximum relative error in prediction of surface roughness is 9%.