Hot deformation characteristics of NiTiV shape memory alloy and modeling using constitutive equations and artificial neural networks

In this work, isothermal compression tests over wide ranges of deformation parameters (deformation temperatures and strain rates) are carried out for investigating the hot deformation behaviors of low-alloyed ultrahigh strength steel 30CrMnSiNi2A. For clarifying the effects of deformation parameters on microstructural evolution and dynamic recrystallization (DRX) nucleation mechanisms, the optical microscope (OM) and transmission electron microscope (TEM) are employed. It is found that DRX easily take place and the initial microstructures are greatly refined under low deformation temperatures (920 and 970 ° C ) or high strain rates (1 and 10 s -1 ). The DRX behaviors are nearly completed with the strain rate decreasing or deformation temperature increasing, and the DRXed grain growth takes place. Under high strain rates, the uncompleted DRX aggravates the inhomogeneous grain distribution, thus the average grain size initially decreases and then increases with the strain rate decreasing. It is also found that discontinuous DRX behavior characterized by grain boundary migration takes place under various deformation parameters. Besides, the dislocation cells and subgrains are detected under the strain rate of 10 s -1 , which means continuous DRX mainly takes place under high strain rate. According to the experimental data, an Arrhenius type model and a physically-based model are constructed, respectively. The good agreements between the reproduced and measured stresses indicate that both models enjoy the excellent capability in reproducing the hot deformation behaviors of 30CrMnSiNi2A steel.

Hot deformation behaviors of low-alloyed ultrahigh strength steel 30CrMnSiNi2A: Microstructure evolution and constitutive modeling

Effects of Sm element addition on the workability and microstructure evolution of Mg-Gd-Y-Zr alloy during hot deformation

To better understand the elevated temperature flow behaviors of 30Si2MnCrMoVE low-alloy ultra-high strength steel, hot compressive deformation tests were performed at a wide range of forming temperatures (1123–1423 K) and strain rates (0.01–1s−1). The constitutive equations of the material were established through the physical-based modified Zerilli-Armstrong (ZA) and phenomenological Arrhenius-type models, along with the optimized ZA model of strain compensation constructed based on the modified ZA model. Suitability of these models were evaluated by comparing the correlation coefficient and absolute average error between the prediction and experimental data, its ability to predict the deformation behavior, and the amount of calculation required for the material constants. Results show that the modified ZA model cannot accurately describe the flow behavior of the 30Si2MnCrMoVE steel in the abovementioned hot working zone. The optimized ZA model and Arrhenius-Type model based on strain compensation require similar computational effort, and both can effectively predict the high temperature flow behavior of 30Si2MnCrMoVE steel. Relatively, the Arrhenius type model has the highest prediction accuracy. 

A Comparative Study on Modified and Optimized Zerilli-Armstrong and Arrhenius-type Constitutive Models to Predict the Hot Deformation Behavior in 30Si2MnCrMoVE Steel

It is important to model the flow behavior of an aged aluminum alloy AA6082 as an extrusion material before design and optimize of the forming process. In this study, the isothermal compression tests were carried out on Gleeble-3800 thermal simulator in the temperature range of 423−773 K and the deformation condition of 0.01–1.0 s−1 to study the cold temperature and hot deformation behavior of aluminum alloy AA6082. Considering the experimental error, the discussion based on friction and temperature correction is carried out. According to the modified data, based on the traditional Arrhenius constitutive model, a method combining the strain compensation Arrhenius model and PSO-BP neural network was proposed to describe the flow behavior of aluminum alloy AA6082. The prediction ability and stability of the model are evaluated by using the linear correlation coefficient(r), the average relative errors (AARE), the Root mean square errors(RMSE) and the relative errors (RE) in statistical analysis. The results show that the 3-10-8-1 double hidden layer neural network model based on PSO-BP has higher effect in predicting the flow characteristics of aging aluminum alloy AA6082 than that of Arrhenius model based on strain compensation. The linear correlation coefficient, mean relative error and root mean square error are 0.9996 %, 2.001 % and 1.665 MPa respectively.

Research on compression deformation behavior of aging AA6082 aluminum alloy based on strain compensation constitutive equation and PSO-BP network model

In this work, the hot deformation behavior of Alloy 925 with intermediate strain rates of 0.01s−1-10s−1 ranging from 900 °C to 1150 °C has been investigated through the isothermal hot compression tests. Some features of the flow curves corresponding to negative strain rate sensitivity have been observed and discussed associated with the microstructural evolution, showing the nucleation mechanism of dynamic recrystallization and the occurrence of dynamic strain ageing. Besides, we have employed two typical constitutive models, namely, Arrhenius model and backpropagation artificial neural network (BP ANN) model to describe the flow behavior, and also developed a modified BP ANN model based on genetic algorithm (GA-BP ANN). The results show that the GA-BP ANN model has the highest accuracy and stability for predicting the flow stress. The correlation coefficient between the predicted and experimental values is 99.99 %, and the average absolute relative error is only 0.54 %. The comparative investigation on the predicted values of different ANN models reflects that GA can reduce the randomness of initial weights and thresholds of BP ANN and also can further increase the accuracy and stability of ANN model. Moreover, the increasing number of input training data can improve the prediction performance of neural network. For a single-layer neural network, 12 hidden layers can effectively ensure the reliability of the constitutive model.

Dynamic behavior and modified artificial neural network model for predicting flow stress during hot deformation of Alloy 925

In the present work, the orientation characteristics of residual grains during hot deformation of an age-hardening Ni-Fe-Cr alloy (Alloy 925) at different conditions were systematically analyzed through high-resolution electron back-scatter diffraction Based on the measurement of the kernel average misorientation, the density of geometrical necessary dislocations (GNDs) was further calculated The orientation-dependent deformation mechanism of the residual grains was also discussed using Schmid factor difference ratio (SFDR) analysis The results show that the deformed microstructure features typical “necklace” structures Many distorted twin boundaries can be observed within the residual grains at 950 °C When the deformation temperature increases to 1150 °C, the volume fraction of dynamic recrystallization (DRX) increases, leading to the extensive formation of primary Σ3 twin boundaries Additionally, the GNDs are widely distributed in the residual grains, while they are rare for the recrystallized grains The maximum GND density value can be obtained at the interface of “soft–hard” grains The GND density also increases at higher strain rates, and the number of DRX grains significantly affects the distribution of the GND density Moreover, based on the calculations and SFDR analysis, it can be summarized that the {100} and {111} grains are prone to deform in the uniserial slip mode and the multiple slip mode, respectively

Orientation-Dependent Characteristics for Residual Grains during Hot Deformation of Nickel-Based Alloy 925

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Dynamic behavior and modified artificial neural network model for predicting flow stress during hot deformation of Alloy 925

Orientation-Dependent Characteristics for Residual Grains during Hot Deformation of Nickel-Based Alloy 925