A Texture Removal Method for Surface Defect Detection in Machining

Abstract

Surface defect detection in mechanical processing mainly adopts manual inspection, which has certain issues including strong dependence on manual experience, low efficiency, and difficulty in online detection. A surface texture elimination method based on improved frequency domain filtering in conjunction with morphological sub-pixel edge detection is put forward in order to address the aforementioned issues with machining surface defects. Firstly, ascertain whether textures exist in the image and determine their feature values using the grayscale co-occurrence matrix. The main energy direction of the textured surface in the frequency domain was then obtained by applying the Fourier transform to the processed surface. An elliptical domain narrow stopband was designed to reduce the energy in the band region corresponding to the processed surface texture and eliminate the processed surface texture. Finally, improve morphology and sub-pixel edge fusion to extract surface defect images. Cracks and scratches have a detectable width of 0.01 mm, a detection accuracy of 97.667%, and a detection time of 0.02 s. Therefore, the combination of machine vision and texture removal technology has achieved the detection of surface scratches and cracks in machining, providing a theoretical basis for defect detection in workpiece processing.