An investigation on the aging responses and corrosion behaviour of A356/SiC composites by neural network: The effect of cold working ratio

Abstract

In the present study, an artificial neural network model has been used for predicting the corrosion behaviour, aging and hardness responses of aluminium-based metal matrix composites reinforced with silicon carbide particle. Hyperbolic tangent sigmoid and linear activation functions are employed as the most appropriate activation function for hidden and output layers, respectively. The developed artificial neural network model is used to predict the corrosion current density, peak aging time and peak hardness of the composites. Feed forward back propagation neural network has been trained by Levenberg Marquardt algorithm. The regression correlation coefficients ( R2) between the predicted and the experimental values of the corrosion current densities are found as 0.99986, 0.99629 and 0.99671 for the training, testing and validation datasets, respectively. Also, some case studies have been predicted by artificial neural network model. Test results indicate that the proposed network can be used efficiently for the prediction of the polarization response, peak aging time and peak hardness of the composites for different SiC volume fractions and deformation ratio without using any experimental data.