Development of a smart paint defects detection system

油漆缺陷表现为最佳涂装条件不可避免的变化，如粉尘、工人失误、温度变化等，并表现为表面变化。有些缺陷很容易发现，例如裂缝或划痕。否则，它们更难被检测到，例如粘在零件上的小颗粒。这些缺陷导致零件不符合质量控制标准，导致零件报废。熟练的操作员通常不会检测到80%的小缺陷。这些未检测到的缺陷构成了降低产品寿命的腐蚀源。Magna Exteriors Inc.的子公司Rollstamp Mfg.是主要汽车公司的零部件供应商。目前的汽车涂装零件质量控制系统是基于100%人工目测的零件。此外，手动过程不允许进行数据收集和快速反馈，从而无法确定问题的原因并相应地调整涂装过程。该项目建立在与RollStamp工程团队的协同关系以及通过NSERC/Engage和OCE/VIP拨款检测火山口涂料缺陷的经验基础上，火山口涂料缺陷是行业确定的典型涂料缺陷之一。该项目旨在开发下一代智能生产级油漆缺陷检测系统Smart Inspector，该系统能够以可接受的精度和周期对不同零件几何形状的不同油漆缺陷进行检测和分类。这将需要广泛的数据收集和使用机器学习策略来开发用于缺陷检测和分类的先进算法。该项目旨在协助行业合作伙伴努力提高其运营效率和产品质量，这对加拿大经济是直接有益的。此外，由于市场份额的增加，它将提供培训机会，创造更高报酬的工作和更多的就业机会。

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.