Investigation of Service Life Prediction Models for Metallic Organic Coatings Using Full-Range Frequency EIS Data

Various service life prediction models of organic coatings were analyzed based on the acquirement of the measurement of Electrochemical Impedance Spectroscopy (EIS) from indoor accelerated tests. First, some theoretical formulas on corrosion lifetime predictions of coatings were introduced, followed by the comparative assessment of four practical prediction models in view of prediction accuracy in application. The prediction from impedance data at single low frequency |Z| 0.1 Hz, the classical degradation kinetics, and proposed improved degradation kinetics model, as well as a self-organized neural network prediction based on sample detection, were focused in this paper. The standard AF1410 plates employed as the metallic substrates were coated with sprayed zinc layer, epoxy-ester primer and polyurethane enamel layer. The accelerated experiments which mimicked coastal areas of China were carried out with the specimens after surface treatment. The assessment of results showed that the proposed improved degradation kinetics model and neural network classification model based on the full range of frequency data obviously have higher prediction accuracies than the traditional degradation kinetics model, and the prediction precision of the sample detection-based neural network classification was the highest among these models. The study gives some insights for coating degradation lifetime prediction which may be useful and supportive for practical applications.