Study of Support Vector Regression for the in-process optical measurement of surface roughness using laser scattered images

Abstract

Results from an optical technique for in-process measurement of surface roughness using laser scattered images are presented. Based on light scattering principle, an experimental system that consists of a collimated laser diode, a screen and a CCD sensor is designed to measure surface roughness. The parameters such as a modified scattering feature, bright points ratio and bright grey ratio are obtained from the scattered images. A machine learning technique called support vector regression (SVR) is developed to determine surface roughness. The three features are chosen as input parameters, and surface roughness is selected as output of the SVR model. Experimental results show that the proposed method is effective for the optical measurement of surface roughness with a satisfactory accuracy. The proposed system combined with a transparent window method can be applied to in-process measurement of the surface quality of a machined component.