Artificial Neural Network Modeling of ECAP Process

Abstract

Equal channel angular pressing (ECAP) is a type of severe plastic deformation procedure for achieving ultra-fine grain structures. This article investigates artificial neural network (ANN) modeling of ECAP process based on experimental and three-dimensional (3D) finite element methods (FEM).In order to do so, an ECAP die was designed and manufactured with the channel angle of 90° and the outer corner angle of 15°. Commercial pure aluminum was ECAPed and the obtained data was used for validating the FEM model. After confirming the validity of the model with experimental data, a number of parameters are considered. These include the die channel angles (angle between the channels Φ and the outer corner angle Ψ) and the number of passes which were subsequently used for training the ANN. Finally, experimental and numerical data was used to train neural networks. As a result, it is shown that a feed forward back propagation ANN can be used for efficient die design and process determination in the ECAP. There is satisfactory agreement between results according to comparisons.

Keywords:

• Aluminum
• ANN
• ECAP
• FEM
• Grain