Image-Based Inspection Technique of a Machined Metal Surface for an Unmanned Lapping Process

This paper presents a new machine vision framework for the efficient examination and classification of surface textures on medium- and large-sized mold products, such as used for automobiles, TVs, and refrigerators. Existing techniques, which are based on the hands and eyes of skilled workers, are inconsistent and time-consuming. Although there are many types of precise surface inspection and measurement methods, most are difficult to apply at industrial sites or by finishing robots due to problems such as speed, setup limitations, and robustness. This paper proposes two techniques based on image processing that aims to automate surface inspection during an unmanned lapping process that is mainly employed to eliminate milling tool marks. First, both the shape of the reflected light and the intensity of the captured near-field contrast image right after the reflected specular are used to determine the machined surface state, and the presence of tool marks as the line light source scans counter-clockwise. Second, the photometric stereo technique is used to detect surface scratches through the normal map that recovers the surface. The proposed techniques show localized machined patterns and classify them with high accuracy.