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Metals 2017, 7(7), 274; doi:10.3390/met7070274

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

1
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
2
School of Reliability and System Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 17 June 2017 / Revised: 13 July 2017 / Accepted: 13 July 2017 / Published: 17 July 2017
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Abstract

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. View Full-Text
Keywords: EIS; service life prediction; degradation kinetics; improved degradation kinetics; neural networks EIS; service life prediction; degradation kinetics; improved degradation kinetics; neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xu, Y.; Ran, J.; Dai, W.; Zhang, W. Investigation of Service Life Prediction Models for Metallic Organic Coatings Using Full-Range Frequency EIS Data. Metals 2017, 7, 274.

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