This Article Full Text (OnlineFirst PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Pidaparti, R. M. Articles by Sharma, H. Search for Related Content Social Bookmarking                         What's this?

Degradation Experiments and Analysis of Corrosion Damage

¹ Department of Mechanical Engineering Virginia Commonwealth University, Richmond, VA 23284, USA
² Department of Chemical Engineering University of Wisconsin, Madison, WI 53706, USA

^* To whom correspondence should be addressed.

	Abstract

Corrosion is one of the most damaging mechanisms in many engineering structures. To better understand how corrosion growth process takes place in AA 2024-T3 materials, research is being conducted to capture and systematically characterize the corrosion process, and develop simulation models. The objective of this study is to systematically investigate the degradation of major chemical elements (Al and Cu) in AA 2024-T3 as a function of the exposure time and electrochemical parameters during the corrosion process through experiments and develop an artificial neural network (ANN) model for the analysis and prediction. Experiments were conducted on AA 2024-T3 specimens under controlled electrochemical conditions. The chemical element map was developed through energy dispersive spectrometry technique for evaluation purposes. Based on the experimental data, an ANN model is developed for the analysis and prediction of degradation of major chemical elements at various exposure time and electrochemical parameters during the corrosion process. A very good performance of the neural network is achieved after training and validation with the experimental data. The degradation of the major chemical elements and material loss at various electrochemical parameters as a function of time was studied through ANN analysis.

Key Words: artificial neural networks, corrosion, degradation, aircraft aluminum, analysis, prediction.

First published on February 16, 2009
International Journal of Damage Mechanics 2009, doi:10.1177/1056789508098586


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?