An Artificial Neural Network (Ann) Solution To the Prediction of Age-Hardening and Corrosion Behavior of an Al/Tic Functional Gradient Material (Fgm)

Abstract

In this theoretical study, the prediction of the corrosion resistance and age-hardening behavior of an Al/TiC functional gradient material (FGM) has been investigated by using the artificial neural network (ANN). The input parameters have been selected as TiC volume fraction of the composite layers, aging periods of the composite, environmental conditions, and applied potential during the corrosion tests. Current and microhardness were used as the one output in the proposed network. Also, a new three-layered composite has been imaginarily designed to demonstrate the predictive capability and flexibilities of the ANN model as a case study. Artificially aging (T6) process and potentiodynamic scanning (PDS) tests were used for heat-treating and corrosion response of the FGS, respectively. The results showed that the generated PDS curves of the FGM and calculated corrosion parameters of the case study are quite near and in acceptable limits for similar composites obtained values in experimental studies. Besides, this study has been a great success in predicting peak-aging times and its corresponding hardness values more precisely.