Predicting coating performance through the integration of Pattern Recognition algorithm via Imaging and electrochemical testing (C2026-00422)

Victor Ponce, heather eich, Rishi Gupta, Sasha George, Andy Nowasielski, Homero Castaneda

Coating performance is critical 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 metallic corrosion. Many methods have been developed to study coating damage. In this work, we present a simple and effective method that combines electrochemical impedance spectroscopy (EIS) with image-based pattern recognition to detect coating degradation over time. To capture consistent images of the coated samples, we design an imaging station. The pictures were then analyzed using pattern recognition techniques to detect and measure corrosion spots. Two types of coatings, a polymer-based and inorganic particles in polymer matrix, 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.