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.
A reliable constitutive model is a prerequisite to simulate a new complex forming technique, which is represented by the near-net shape forging process of aluminum wheels in this study. The aim of the present work was to identify the physical-based constitutive model parameters of Al-Zn-Mg alloy via the inverse analysis method based on experimental data and numerical analysis: the stress–strain curves at different temperatures and strain rates were obtained based on hot compression tests. On the basis of the shape of the compressed specimens and experimental force–displacement data, the friction coefficients and the optimized physical-based constitutive model were determined by using two-times inverse analysis techniques. Results showed that the global average error between the predicted and experimental force–displacement curves was only 3.8%. Then, thermo-mechanical finite element models were built in the Deform-3D software to simulate the two-stage forging processes of the near-net shape forging of aluminum alloy wheels, and the results showed that the predicted load–stroke curves were in good agreement with the experimental ones in all forging stages, which verified the prediction accuracy of the optimized physical-based constitutive model. In addition, the identification of the physical-based constitutive model parameters by the inverse analysis method provides a theoretical basis for formulating and optimizing the near-net shape forging process parameters of aluminum alloy wheels.
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