Exploring the use of machine learning for improving the efficiency of coating performance evaluation.

Abstract

The use of machine learning was explored in the context of electrochemical impedance spectroscopy (EIS), with the purpose of overcoming some of its inherent complexities and increasing the efficiency in its use for coating performance evaluation . For this project, EIS and visual inspection data from marine coatings exposed to accelerated corrosion tests for approximately 2.5 years were applied to machine learning techniques to acquire a prototyped classification-type algorithm. Also, electrochemical tests–including EIS and alternative methods–were applied to coated samples immersed in 5 wt.% NaClto generate datafor the training, testing and validation of fitting-type machine learning algorithms, “prepared as a proof of concept”. An experimental setup using automatablecomponentswas adopted, which surpassed the necessity of preparing and performing each electrochemical test one-by-one.Overall, the results have shown that machine learning approaches have the potential to overcome several complexities related to EIS, and this combination of knowledges should be further exploited.