Nickel-based alloys containing high amount of Cr are desirable materials for the production of critical components that operate under high temperature and severe corrosion conditions. The description of flow behaviors for these alloys is key to designing the hot formation process for achieving the excellent mechanical properties of components. This work aims to study the flow behaviors of Ni-38Cr-3.8Al alloy during thermal deformation. A series of isothermal compression experiments were carried out at temperatures of 1148 K, 1223 K, 1298 K, 1373 K, 1448 K and 1523 K, and strain rates of 0.01 s-1, 0.1 s-1, 1 s-1 and 10 s-1. Results show that the obtained true stress-strain curves exhibit the softening characteristics of dynamic recrystallization (DRX) and dynamic recovery (DRV). The flow stress decreases with temperature increasing and strain rate decreasing. To further quantitatively describe the flow behaviors, an improved segmented Arrhenius constitutive equation was developed according to the softening characteristics under different temperatures. In these equations, the variable coefficients, including activation energy Q and material constants (structure factor A, stress exponent n, stress parameters α), were fitted as the polynomial functions of true strain. Furthermore, a typical Arrhenius constitutive equation was also solved. The comparisons between experimental stress and predicted results obtained from the typical and improved constitutive equations were conducted, and the correlation coefficient (R) and average absolute relative error (AARE) were calculated as 0.932, 15.83%, and 0.9963, 5.61%, respectively. It suggests that the improved constitutive equation can adeptly describe the flow behaviors of Ni-38Cr-3.8Al alloy.

In order to achieve hot processing products with expected microstructures, the construction of corresponding relationships between micro-evolution mechanisms and hot processing parameters is essential. In this study, such corresponding relationships of as-cast AlCu4SiMg alloy were constructed by double evaluating processing maps and Zener-Holloman (Z) parameter maps. Based on the stress-strain data obtained from a series of isothermal compression experiments, the processing maps of AlCu4SiMg alloy were constructed at the strain of 0.3, 0.5, 0.7 and 0.9 s-1. The processing maps revealed that the optimal hot deformation parameter windows corresponding to dynamic recrystallization (DRX) micro-evolution mechanism mainly appear at high temperature and moderate strain rate. On the other hand, the response maps of Z parameter at discrete strains were constructed, and the ideal processing windows were calibrated at the domains with relatively low lnZ-value. A phenomenon was found that the optimal deformation parameter windows identified by Z parameter are more conservative than those identified by processing map. By integrating processing maps and Z parameter maps, the optimal processing parameter windows corresponding to DRX micro-evolution mechanism for AlCu4SiMg alloy were finally obtained.

Hot compression tests of as-cast Ti-6Al-2Zr-1Mo-1V alloy in a wide temperature range of 1073-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-1500 machine. The hot flow behaviors of Ti-6Al-2Zr-1Mo-1V alloy show highly non-linear relationships with strain, strain rate and temperature. In order to accurately and effectively characterize the complex flow behaviors, support vector regression (SVR) which is a machine learning method was combined with Genetic Algorithm (GA) to characterize the flow behaviors, namely, the GA-SVR. The study abilities, generation abilities, and modeling efficiencies of the improved Arrhenius-type constitutive model, ANN, and GA-SVR for flow behaviors of as-cast Ti-6Al-2Zr-1Mo-1V alloy were detailedly compared. Comparison results show that the study ability of the GA-SVR is as strong as the ANN. The generation abilities and modeling efficiencies of these models were shown as follows in ascending order: the improved Arrhenius-type constitutive model < ANN < GA-SVR. Based on the established GA-SVR, the continuously three-dimensional relationships among flow stress, temperature, strain, and strain rate were constructed, which improve the simulation accuracy and related research fields where stress-strain data play important roles.

The electric upsetting process is an excellent electrically assisted preforming technique with electrical-thermal-mechanical multi-field coupling characteristic. The secondary upsetting defect in electric upsetting with inappropriate parameters combinations could result in folds in subsequent forging process. In order to analyze the formation process of the secondary upsetting defect, the finite element model of electric upsetting was constructed based on the multi-field coupling solver platform, MSC.Marc. It is concluded through the numerical simulation with inappropriate parameters combination that the main factor causing the secondary upsetting defect is the large temperature decrease of the deformed portion after the formation of drum-shape in the preliminary stage of electric upsetting. Subsequently, the secondary upsetting defect has been successfully avoided via a three-stage current mode. Finally, a novel optimization method based on close-cycle control of current in numerical simulation was established by developing a current subroutine in MSC.Marc, which was proved to be effective for the elimination of the secondary upsetting defect.

Hot compressions of as-cast AZ80 magnesium alloy in a wide temperature range of 523-673 K and strain rate range of 0.01-10 s–1 with a height reduction of 60% were conducted by a Gleeble-1500 thermo-mechanical test simulator. The hot flow behaviors show highly non-linear intrinsic relationships with temperature, strain and strain rate. In order to model the complicated flow behaviors, error back-propagation algorithm, a representative method to minimize the target error, was selected to train the artificial neural network. A comparative study was made on the predictabilities of the improved Arrhenius-type and BP-ANN model by using two standard statistical parameters including correlation coefficient (R) and average absolute relative error (AARE). Comparison results show that the well-trained BP-ANN has higher prediction accuracy. Three highlight applications were presented. Firstly, the strain-stress data volume was expanded by BP-ANN predictions above experimental conditions. Secondly, the expanded data were applied in the simulations of isothermal compressions, and high simulation accuracy for the load-stroke curve was achieved. Thirdly, a three-dimensional (3D) interaction space of stress, strain, temperature and strain rate was constructed based on the intensive data, which supplies the stress data to arbitrary temperature, strain rate, and strain.

In 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. In addition, the predicted data outside of experimental conditions were obtained, indicating good prediction potentiality of the developed ANN model. The θ -σ 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, A 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.

In order to study the hot working behaviour of as-cast AZ80 magnesium alloy, a series of isothermal upsetting tests with height reduction of 60% were performed at the temperature range of 523-673 K and the strain rate range of 0.01-10 s-1 on a servo-hydraulic Gleeble-1500 machine simulator. Based on the regression analysis for Arrhenius type equation of flow stress, the activation energy of deformation was determined to be Q = 168.6606 KJ·mol-1, and material constants , n and α in the equation also were calculated respectively to predict the peak stress during hot deformation. A 7th order polynomial was used to represent the influence of strain on these parameters (i.e Q, ln, n and α). The Arrhenius equation was further developed by taking account of the effect of strain on the flow stress to describe the flow behavior during the whole experimental temperature and strain rate range. Then comparison between the predicted results and test results shows that the correlation coefficient and average absolute relative error is 0.99 and 6.63%, respectively. The flow stress predicted by developed equation can give an accurate and precise description of the flow behaviour for AZ80 magnesium alloy.