Finding and Quantitative Evaluation of Minute Flaws on Metal Surface Using Hairline

A method to detect minute flaws on metal parts is proposed to remove defective parts before assembling in a factory. The input grayscale images of metal parts are directly used to recognize flaws without any image conversion to shorten recognition time. The recognition problem to find flaws and detect their position on the metal parts is converted to another problem that searches for the maximum peak and the variables producing the peak. Then, the recognition problem can be treated as an optimization problem, and this conversion allows us to utilize high genetic algorithm performances in optimization. The effectiveness and problems of the proposed method are studied on the standing points of recognition speed and quantitative recognition ability. Based on the analysis, we furthermore improved our system to increase the flaw detection rate; the lighting direction was changed to find the best lighting condition that can emphasize the contrast between the metal surface and the flaw by using the reflection character of the hairline on the metal, which is resulted by a polishing process