Prediction of Mechanical Properties of Equal Channel Angular Rolled Al6061 Alloy Sheet Using Artificial Neural Networks and Nonlinear Regression

Equal channel angular rolling (ECAR) is a severe plastic deformation (SPD) process in order to achieve ultrafine-grained (UFG) structure. In this paper, the mechanical properties of ECAR process using artificial neural network (ANN) and nonlinear regression have been illustrated. For this purpose, a multilayer perceptron (MLP) based feed-forward ANN has been used to predict the mechanical properties of ECARed Al6061 alloy sheets. Channel oblique angle, number of passes and the route of feeding are considered as ANN inputs and tensile strength, elongation and hardness are considered as the outputs of ANN. In addition, the relationship between input parameters and mechanical properties were extracted separately using nonlinear regression method. Comparing the outputs of models and experimental results shows that models used in this study can estimate the mechanical properties appropriately. Where, the performance of ANN model is better than the correlations to predict mechanical properties. Finally, the developed outputs of trained neural network model are used to analyze the effects of input parameters on tensile strength, elongation and hardness of ECARed Al6061 alloy sheets. The results showed that the ANN model, without highly expensive tests and experiments, is an efficient tool to predict the mechanical properties of ECARed Al6061 sheets.