Predicting Coating Performance through the Integration of Pattern Recognition Algorithm via Imaging and Electrochemical Testing

Abstract Coating performance is very important for protecting automotive parts from damage caused by the environment. Conditions such as humidity, salty roads, and temperature changes can lead to coating failure and corrosion of the metal underneath. Many methods have been developed to study coating damage, but most do not combine both electrochemical and visual analysis. This study introduces an image-based approach combined with electrochemical impedance spectroscopy (EIS) to evaluate coating degradation. The controlled imaging setup allows consistent monitoring of the coating surface, while pattern recognition techniques enable the early detection of corrosion spots not visible to the naked eye. This integrated method provides a faster and more objective way to assess coating performance under accelerated corrosion conditions.To capture clear and consistent images of the coated samples, we built a custom photography station with a fixed camera and controlled lighting. This setup avoids changes in the images caused by different light levels. The pictures were then analyzed using pattern recognition techniques to detect and measure corrosion spots. The samples were exposed to 5% and 2% NaCl solutions in an accelerated corrosion chamber that runs in cycles of one dry day followed by one wet day. EIS measurements were taken regularly to track changes in the coating’s protective ability, and these results were compared with the image analysis of corrosion spots. This combined method of EIS and pattern recognition gives a better understanding of how coatings fail and helps predict their performance. It can be a useful tool for faster testing and development of coatings in the automotive industry.