Degradation Experiments and Analysis of Corrosion Damage

作者：Pidaparti Ramana M^*; Koombua Kittisak; Abdelsayed Ihab; Sharma Himanshu

来源：International Journal of Damage Mechanics, 2010, 19(2): 197-210.

DOI：10.1177/1056789508098586

• artificial neural networks
• corrosion
• degradation
• aircraft aluminum
• analysis
• prediction

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.

• 出版日期2010-3