Automated Profile Defect Inspection of Car Mirrors

• Main Authors: Yu-Kai Lin, 林宥凱
• Other Authors: Hong-Dar Lin
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/43848010455087917796

碩士 === 朝陽科技大學 === 工業工程與管理系 === 103 === In recent years, auto sales gradually increased in Taiwan. Since car mirrors are standard accessories with cars, the demand of car mirrors also increased and manufacturers pay more emphasis on the increase of product quality. Common defects of car mirrors include: scratches, convex, concave causing surface defect type and tailoring, edging negligence of process causing profile defect type. Currently, the inspection tasks are conducted by human inspectors. Since the profile defects will cause structural damages of car mirrors and reduce ability to withstand stress, the degree of harm even more than the surface defects. In additions, the angle diversity of capturing images makes it is not easy to implement automated defect inspection. Therefore, this study develops an automated profile defect detection system of car mirrors to replace visual inspection personnel from car mirror inspection tasks. Two types of images called front images and side images are captured from two views of real car mirror samples. In this study, the distances of edge points to the object centroid are used to describe the shape of a car mirror. To enhance the profile defects on the mirror surface images, the distances of edge points are applied to 1-D wavelet transform with low-pass filtering. The distance deviations of the edge pints before and after the wavelet filtering process can be distinguished by the model of exponential weighted moving average (EWMA) to identify the defect locations. The proposed approach does not require a standard flawless sample in detection process and derive information to compare with testing images. We only use their own information of testing images to determine whether there are any irregular contour changes. The proposed method were carried out to detect the defect size lager than 0.2 mm. Experimental results show that the proposed system achieves 7% false alarm rate and 86% defect detection rate for front image inspection; 5% false alarm rate and 92% defect detection rate for side image inspection.