Abstract
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 language | English |
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Pages (from-to) | 3070-3077 |
Number of pages | 8 |
Journal | Corrosion Science |
Volume | 52 |
Issue number | 9 |
DOIs | |
State | Published - 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.
Keywords
- A. Aluminum alloys
- C. Neural networks
- C. Pitting corrosion
ASJC Scopus subject areas
- Chemistry (all)
- Chemical Engineering (all)
- Materials Science (all)