Prediction of Recrystallization Structure of 2A12 Aluminum Alloy Pipe Extrusion Process Based on BP Neural Network

Abstract

2A12 aluminum alloy is a high-strength aerospace alloy. During its extrusion process, the extrusion process parameters have a great impact on the microstructure evolution of the extruded products. There are three extrusion process parameters controlled in the actual project, which are the initial temperature of billet, the initial temperature of die and the extrusion speed. Combined with a back propagation (BP) neural network and finite element method (FEM) simulation, based on the constitutive equation and recrystallization evolution process of 2A12 aluminum alloy, this paper establishes a prediction model for the grain size of extruded pipe by these three extrusion process parameters. This paper used a 35MN extruding machine for a production verification of 2A12 pipe. The results show that the predicted grain size is 3% smaller than the actual size.