Prediction of multilayer Cr/GLC coatings degradation in deep-sea environments based on integrated mechanistic and machine learning models

Abstract

Improving the accuracy of coating lifetime prediction on small sample data has been an urgent issue to be addressed. In this paper, based on in-situ electrochemical impedance spectroscopy data of multilayer Cr/GLC coatings, a lifetime prediction formula related to the coating failure mechanism is developed, which provides a quantitative basis for estimating the coating lifetime. Furthermore, the combination of the mechanistic prediction model and the ANN + RF integrated machine learning model can further increase the model’s prediction accuracy, reach 97.9%, and provide a new method for predicting coating performance and lifetime in deep-sea environments.