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.

A deeper insight into the changing states of corrosion during certain exposure circumstances has been investigated by applying Kohonen networks. The Kohonen network has been trained by four sets of samples and tested using another sample. All the sample data were collected during accelerated corrosion experiments and the network took the changing rate of impedance of each cycle as an input. Compared with traditional classification, the Kohonen artificial network method classifies corrosion process into five sub-processes which is a refinement of three typical corrosion processes. The two newly defined sub-processes of corrosion—namely, pre-middle stage and post-middle stage—were introduced. The EIS data and macro-morphology for both sub-processes were analyzed through accelerated experiments. The classification results of the 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 for predicting the failure cycles of polymer coatings.