Use of artificial neural network models to predict coated component life from short-term electrochemical impedance spectroscopy measurements

G. Kumar, R. G. Buchheit

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

The objective of this study was to relate the results from electrochemical impedance spectroscopy (EIS) collected in 24 h to salt spray exposure data collected over 1,500 h for conversion-coated metal surfaces. To develop such a relationship, an approach based on artificial neural networks (ANN) was used. The output of this study was a matrix of weights and threshold values that predicted the salt spray performance of coated components based on EIS results. A model based on phase-angle data input from EIS measurements collected after 24 h exposure to 0.5 M sodium chloride (NaCl) was able to account for 85% of the variation in the salt spray time to failure from a randomly selected subset of the sample population. This exercise illustrates the utility of ANN in corrosion prediction and suggests that they may play a key role in making lifetime predictions for components in service based on laboratory measurements.

Original languageEnglish
Pages (from-to)241-254
Number of pages14
JournalCorrosion
Volume64
Issue number3
DOIs
StatePublished - Mar 2008

Keywords

  • Aluminum alloys
  • Artificial neural networks
  • Conversion coatings
  • Corrosion prediction
  • Electrochemical impedance spectroscopy

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • General Materials Science

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