| Summary: | 碩士 === 朝陽科技大學 === 工業工程與管理系 === 105 === Surface scraping process is usually done by handwork, so the quality will rely heavily on worker’s experience, skill level and stability. In addition to flatness improvement, scraping also provides micro oil pockets to enhance the lubrication effect between the flat sliding surfaces. However, due to its handcrafted and trivial nature, inspection of scraped surfaces with bare eyes is prone to unreliable quality. Besides, in most cases, workplace illuminations are insufficient or uneven, and thus, adding more difficulties to image-based inspections. Nevertheless, two indices are still widely used in the industry to determining the quality of scraping: Percentage of points (POP) and Points per square inch (PPI). Therefore, this research will develop a web-based scrap inspection system that captures images of the scraped surface at the worksite, and sends the images to a remote server for image processing and inspection to draw the indices. At first, an adaptive threshold method is performed to cope with the light source problem, then find POP and PPI out of the scrapped image down to a quarter of a square inch. In practice, the images are captured with a CMOS camera via USB interface using a tablet PC, and the calculation are done at a web-linked workstation, then the overall quality and quality distributions are presented on the tablet to simulate a real-time cloud-ready manufacturing management process.
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