Machine Vision for Surface Roughness Assessment of Inclined Components

Many researchers have so far used machine vision and digital image processing for grabbing images of machined surfaces, improving their quality by pre-processing and then analysed them for evaluation of surface finish with a reasonable success. An attempt has been made in this work to capture the images of the surfaces with varying inclinations covering both the sides. The ideal orientation of the surface (flat and horizontal) is found by observing the variation in optical roughness parameters estimated from the grey level co-occurrence matrix as the angle of inclination changes. It is observed that the variation of roughness parameters with respect to angle of inclination also depends on the surface roughness of the component. The optical roughness values obtained by machine vision approach are then subsequently compared with the conventional R(a) as obtained by stylus method and the analysis is presented.