Modeling the environmental dependence of pit growth using neural network approaches

M. K. Cavanaugh, R. G. Buchheit, N. Birbilis

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


Corrosion pits have been shown to nucleate fatigue cracks, and this is a critical issue for aerospace aluminum alloys, which experience a variety of corrosive environments in service. Consequently, modeling pit growth as a function of environment is necessary. In this study, two orientations of AA7075-T651 blocks were boldly exposed in solutions of varying temperature, pH, and [Cl-] for three exposure times. Optical profilometry and Weibull functions were utilized to characterize pit depth and diameter distributions. Artificial neural networks were a powerful tool in effectively modeling maximum pit dimensions and Weibull parameters. In most environments, pit growth followed t1/3 kinetics.

Original languageEnglish
Pages (from-to)3070-3077
Number of pages8
JournalCorrosion Science
Issue number9
StatePublished - Sep 2010

Bibliographical note

Funding Information:
The authors gratefully acknowledge the support and financial assistance of the American Society for Engineering Education’s National Defense Science and Engineering Graduate Fellowship. Aspects of this work are sponsored by Defense Advanced Research Projects Agency under contract HR0111-04-C-0003, Northrop Grumman serving as the prime contractor. Additionally, the authors gratefully acknowledge the state government of Victoria (DIIRD) for funding of the Victorian Facility for Light Metals Surface Technology.


  • A. Aluminum alloys
  • C. Neural networks
  • C. Pitting corrosion

ASJC Scopus subject areas

  • Chemistry (all)
  • Chemical Engineering (all)
  • Materials Science (all)


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