Abstract
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.
Highlights
A crucial issue in manufacturing, when accurate inspection and rapid production are necessary, is the designation and inspection of machined metal surfaces
In the 125 mm milling case (Fig. 14a2), bright chatter tool marks are shown on both sides of the top, because there is faint reflected light even in the range where reflection should not occur due to chatter marks
An image-based machined surface inspection based on an image processing technique was developed for applications in the process of unmanned lapping
Summary
A crucial issue in manufacturing, when accurate inspection and rapid production are necessary, is the designation and inspection of machined metal surfaces. The automatic inspection and detection of defects using image processing is an area of machine vision that is widely adopted in many industrial fields It is used for high-throughput quality control in production systems, such as the detection of flaws on manufactured surfaces of automobiles or mobile phones [1, 2]. The idea is to design autonomous devices that automatically examine and improve the performance of traditional inspection systems that depend heavily on human inspectors during the lapping process for a mold product surface. This task is usually repetitive and labor intensive, and it might involve exposure to radiation, high noise levels, metal dust, and chemical environments, leading to health problems such as musculoskeletal and lung diseases.
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