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A key functional role is served by the helical surfaces of carbide end mills that can be manufactured during diamond grinding wheel. Localized changes in the form of the helical surface can be caused by abrasion, high pressure, and grinding wheel wear. Therefore, it is extremely important to measure the physical samples of products with a helical surface according to the criterion of profile accuracy, rake angle and core diameter. A specialized inspection machine in reflected light can be used to obtain images across the helical groove. Manually extracting a number of defects from photos takes time. Using defect recognition algorithms, effective and quick quality control of a ground helical surface can be established. As a result, effective surface quality control can be achieved in the machine tool industry. In this study, an innovative approach to determine a defect's shape and location as well as an algorithm for removing it are presented. Both of these approaches are integrated into the technological process used to manufacture products with helical surfaces. With the goal to recognized create suggestions for image analysis using different image levels, the suggested approach provides logically smoothing histograms and limiting contrast as an image pre-processing, based on an analysis of images with useful and faulty parts. Achieved successful extraction of areas of adhesive, diffusion, abrasion and chips from the image through post-processing. The article presents a new approach to recognizing adhesive and diffusion defects on the helical surface of a mill after grinding. When developing this approach, it was revealed that areas with alternating profile changes are most susceptible to the formation of defects under conditions of increased heating of the working area, and specialized inductors for searching for defects in localized areas according to the criterion of pixel brightness intensity were proposed.
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