Surface texture dependence on surface roughness by computer vision

Abstract

A non-contact, full field vision technique is presented to determine the surface roughness values. The variation of extracted texture features, roughness (Frgh), on the arithmetic average roughness (Ra) of the test surface is studied. The effects of magnification and aperture size of the imaging system on the extracted surface features are also examined. The vision system offers a fast and accurate method for the on-line automated surface roughness inspection of machined components.