Development of a smart paint defects detection system

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Paint defects appear as an inevitable variation of the optimum painting conditions, e.g. Powder, worker mistakes, temperature variation, etc. and show up as surface alterations. Some defects are easily detectable, e.g. cracks or scratches. Otherwise, they are more difficult to detect, e.g. Small particles bonded to the part. These defects lead to parts not meeting the quality control standards, resulting in the rejection of the part. Skilled operators usually do not detect 80% of minor defects. These non-detected defects constitute corrosion sources that reduce the life of products. Rollstamp Mfg., a division of Magna Exteriors Inc., is a part supplier for major automotive companies. The current quality control system for painted automotive parts is based on 100% manual visual inspection of parts. Additionally, the manual process does not allow for data collection and rapid feedback that can be used in identifying the cause of the problem and adjusting the painting process accordingly. The project builds on the synergetic relation with Rollstamp engineering team and the experience gained through an NSERC/Engage and OCE/VIP grant for detection of crater paint defect, one of typical paint defects identified by industry. The project aims to develop the next-generation smart production-grade paint defects detection system, the Smart Inspector, that is capable of detection and classification of different paint defects for different part geometries with acceptable accuracy and cycle time. Which will require extensive data collection and use of machine learning strategies to develop advanced algorithms for defects detection and classification. The project aims to assist the industry partner efforts to increase efficiency of its operation and quality of its products which is a direct benefit to the Canadian economy. In addition, it will provide training opportunities, create higher paying jobs and more employment opportunities due to increased market share.