The EIS‐based Kohonen neural network for high strength steel coating degradation assessment

Abstract

Electrochemical impedance spectroscopy (EIS) method is used for a long‐term and in‐depth study on the failure analysis of polymer coatings. With the assistance of neural networks, a deeper insight into the changing states of corrosion during certain exposure circumstances has been investigated by applying specific Kohonen intelligent learning networks. The Kohonen artificial network has been trained by using 4 sets of samples from sample 1# to sample 4# with unsupervised competitive learning methods. Each sample includes up to 14 cycles of EIS data. The trained network has been tested using sample 0# impedance data at 0.1 Hz. All the sample data were collected during exposure to accelerated corrosion environments, and it took the changing rate of impedance of each cycle as an input training sample. Compared with traditional classification, Kohonen artificial network method classifies corrosion process into 5 subprocesses, which is refinement of 3 typical corrosion processes. The 2 newly defined subprocesses of corrosion, namely, premiddle stage and postmiddle stage were introduced. The EIS data and macro‐morphology for both subprocesses were analyzed through accelerated experiments that considered general atmospheric environmental factors such as UV radiation, thermal shock, and salt fog. The classification results of Kohonen artificial network are highly consistent with the predictions based on impedance magnitude at low frequency, which illustrates that the Kohonen network classification is an effective method to predict the failure cycles of polymer coatings.