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Just as in all sectors, the quality control process affects the concepts of quality, cost and customer satisfaction directly or indirectly in the automotive sector. Recently, detecting defects with image processing technology in quality control has found a wide application area. One of the manufacturing methods required in the production of some metal parts in the automotive industry is the cataphoresis coating process. Many and various types of visual defects occur at this stage. In order to notice these defects, there is a need to switch to a human-independent and technological quality control process. There are studies on how image processing technology will detect the visual defects on the part and how it will affect quality and customer satisfaction. This project aims to offer a model that defines how digital image processing with software and hardware systems will be adapted, become suitable for mass production, transportation of the parts and design the layout to cataphoresis coating process using artificial intelligence. Thus, with the adaptation of the system to the quality control process, an improvement in cost, delivery time and work efficiency will be achieved, the Industry 4.0 journey will go one step further.

A Model Proposal for the Adaptation of a System That Can Detect Cataphoresis Coating Defects with a Deep Learning Based Im