Identification of the scraping quality for the machine tool using the smartphone
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The technology for scraped surface detection remains experimental. It is mainly performed visually by experienced technicians, which is neither objective nor standardized. Since the scraping technique is the key to the accuracy and the performance of the machine, a method to determine the characteristics of the scraping surface in a quick, objective way is proposed, in order to ensure the quality of scraping. The scraping surface is photographed using a smartphone, and an Android application is developed using a Java image processing algorithm. A square inch of image is then captured on the screen, the RGB image is isolated, and its histogram is read. Using a watershed algorithm, the background image is calculated from the boundary, and then binary, filter and morphological image processing algorithms are used. If the light is uniform, a split binary technique is used. The scraping appearance is then calculated and analyzed in points per inch (PPI) and percentage of points (POP). The experimental results indicated that the total area for recognition was approximately 1-in2; PPI = 13; and POP = 30.9%, and recognition time was less than 1 sec. The experimental analysis results in this study were compared to those of two scraping experts; the two errors were both lower than 5%.
KeywordsScraping surface Image processing Android Points per inch Percentage of points
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