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Neural Network Mapping of Corrosion Induced Chemical Elements Degradation in Aircraft Aluminum

Ramana M. Pidaparti1,2, Evan J. Neblett2

Corresponding author, E-mail:
Department of Mechanical Engineering, Virginia Com-monwealth University, Richmond, VA 23284

Computers, Materials & Continua 2007, 5(1), 1-10.


A neural network (NN) model is developed for the analysis and prediction of the mapping between degradation of chemical elements and electrochemical parameters during the corrosion process. The input parameters to the neural network model are alloy composition, electrochemical parameters, and corrosion time. The output parameters are the degradation of chemical elements in AA 2024-T3 material. The NN is trained with the data obtained from Energy Dispersive X-ray Spectrometry (EDS) on corroded specimens. A very good performance of the neural network is achieved after training and validation with the experimental data. After validating the NN model, simulations were carried out to obtain the trends in element degradation with varying pH values, and the results showed correct trends. The preliminary results obtained demonstrate that through a comprehensive study, a better corrosion resistant material can be designed by controlling the degradation of the chemical elements during the corrosion process through neural network methods.


Cite This Article

R. M. . Pidaparti and E. J. . Neblett, "Neural network mapping of corrosion induced chemical elements degradation in aircraft aluminum," Computers, Materials & Continua, vol. 5, no.1, pp. 1–10, 2007.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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