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In order to perform the numerical simulations of forging response and establish the processing parameters for as-extruded 7075 aluminum alloy, the compressive deformation behavior of as-extruded 7075 aluminum alloy were investigated at the temperatures of 573 K, 623 K, 673 K and 723 K and the strain rates of 0.01 s , 1 s -1 and 10 s -1 on a Gleeble1500 thermo-mechanical simulator. Based on the analysis of the effect of strain, temperature and strain rate on flow stress, dynamic recrystallization (DRX) type softening characteristics of the stress-strain curve with single peak were identified. The traditional Arrhenius type model is in favor of the prediction for the flow stress at a fixed strain, and can not satisfy the need of the numerical simulations of various hot forming processes due to the lack of the effect of strain on flow stress. Lin et al. improved Arrhenius type model with a series of variable coefficients as functions of true strain (including activation energy of deformation Q, material constants n and a, and structure factor A) to predict the flow stress during the hot compression. The application has been demonstrated in this work for as-extruded 7075 aluminum alloy. The comparisons between the predicted and experimental results show that, for the worst case, the error in the flow stress estimate is 5.63%, and the max mean error is 3.6%. The developed model provides fast, accurate and consistent results, making it superior to the conventional Arrhenius type model. In further it can be used in computer code to model the forging response of 7075 aluminum alloy mechanical part members under the prevailing loading conditions.

The isothermal compressions of as-forged Ti-10V-2Fe-3Al alloy at the deformation temperature range of 948–1,123 K and the strain rates in the range of 0.001–10 s−1 with a height reduction of 60% were conducted on a Gleeble-3500 thermo-mechanical simulator. The flow behaviors show nonlinear sensitivity to strain, strain rate and temperature. Based on the experimental data, an artificial neural network (ANN) with back-propagation algorithm was developed to deal with the complex deformation behavior characteristics. In the present ANN model, strain, strain rate and temperature were taken as inputs, and flow stress as output. A comparative study on the constitutive relationships based on regression and ANN methods was conducted. According to the predicted and experimental results, the predictabilities of the two models have been evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). The R-value and the AARE-value at strain of 0.5 from the ANN model is 0.9998 and 0.572%, respectively, better than 0.9902 and 6.583% from the regression model. The predicted strain–stress curves outside of experimental conditions indicate similar characteristics with experimental curves. The results have sufficiently articulated that the well-trained ANN model with back-propagation algorithm has excellent capability to deal with the complex flow behaviors of as-forged Ti-10V-2Fe-3Al alloy.

On hot working process, the prediction of material constitutive relationship can improve the optimization design process. Recently, the artificial neural network models are considered as a powerful tool to describe the elevated temperature deformation behavior of materials. Based on the experimental data from the isothermal compressions of 42CrMo high strength steel, an artificial neural network (ANN) was trained with standard back-propagation learning algorithm to predict the elevated temperature deformation behavior of 42CrMo steel. The inputs of the ANN model are strain, strain rate and temperature, whereas flow stress is the output. According to the predicted and experimental results, it indicates that the developed ANN model shows a good capacity of modeling complex hot deformation behavior and can accurately tracks the experimental data in a wide temperature range and strain rate range. On addition, the predicted data outside of experimental conditions were obtained, indicating good prediction potentiality of the developed ANN model. The q -s curves outside of experimental conditions indicate that the predicted strain-stress curves exhibit a typical dynamic recrystallization softening characteristic of high temperature deformation behavior. Through the coupling of the ANN model and finite element model, the hot compression simulations at the temperature of 1273 K and strain rates of 0.01~10 s -1 were conducted. The results show that the predicted constitutive data outside the experimental conditions successfully improved the prediction accuracy of forming load during the FEM simulation.

In order to investigate the compressive deformation behavior of 3Cr20Ni10W2 alloy, a series of isothermal upsetting experiments were carried out in the temperature range of 1203-1403 K and strain rate range of 0.01-10 s -1 on a Gleeble-1500 thermo-mechanical simulator. The results indicate that the flow stress initially increases to a peak value and then decreases gradually to a steady state. The characteristics of the curves are determined by the interaction of work hardening (WH), dynamic recovery (DRV) and dynamic recrystallization (DRX). The flow stress decreases with increasing temperature and decreasing strain rate. The relationship between microstructure and processing parameters is discussed to give an insight into the hot deformation behavior of 3Cr20Ni10W2 alloy. Then, by regression analysis for constitutive equation, material constants (n, α, β, A and Q) were calculated for the peak stress. Further, the constitutive equation along the flow curve was developed by utilizing an eighth order polynomial of strain for variable coefficients (including n, α, Α and Q). The validity of the developed constitutive equation incorporating the influence of strain was verified through comparing the experimental and predicted data by using standard statistical parameters such as correlation coefficient (R) and average absolute relative error (AARE) that are 0.995 and 4.08% respectively.