Automated Profile Defect Detection and Dimensional Measurement of Car Mirrors

• Main Authors: Hsu-Hung Cheng, 鄭旭宏
• Other Authors: Hong-Dar Lin
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/76679613029487152440

碩士 === 朝陽科技大學 === 工業工程與管理系碩士班 === 101 === Car mirrors are indispensable essential in object reflection and play a key role in driving safety. In the production process of the car mirrors, some operations such as baking, electroplating, recesses, edging, etc. could be controlled abnormally. This could easily produce scratches, bubbles, pin holes, chips, the common surface and profile defects on car mirrors. These defects will seriously affect the surface quality of the mirror reflection and increase the driving risk. At present inspection of car mirror in production lines, most tasks are conducted by human inspectors. Human inspection is easy to be interfered by the external objects reflected on the surfaces of mirrors and results in making erroneous judgments of defect inspection. Therefore, this research aims at exploring the automated surface defect inspection and dimensional measurement of car mirrors. In defect inspection of car mirrors, we propose using discrete Fourier transform with high-pass filter and convex hull algorithm to detect surface defects. Meanwhile, this study also proposes using the morphology methods for enhancing object contours then applying convex hull algorithm to detect profile defects on mirror images. In dimensional measurement of car mirrors, we propose using the exponentially weighted moving average control method for detecting small shift variation of mirror edge points, and checking sample points beyond or lower the upper and lower control limits. Experimental results show that the defect detection rate achieves up to 90.47%, the false alarm rate is lower 4.42%, and the dimension classification rate is up to 85.45%. These indicate the proposed system is effective in both of the defect detection and dimensional measurement.