Comparative Study on Constitutive Models for 21-4N Heat Resistant Steel during High Temperature Deformation

Abstract

The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ε ˙ ) of 0.01–10 s−1, the deformation temperature (T) of 1273–1453 K and the maximum deformation is 0.916. The data of the stress-strain (σ-ε) were obtained. Based on the σ-ε data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and ε ˙ of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and ε ˙ , it has a fairly high prediction accuracy. And it can predict flow stress (σ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a Finite Element Method (FEM) of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

Constitutive Equations for Describing the Warm and Hot Deformation Behavior of 20Cr2Ni4A Alloy Steel

“…Table 2 shows coefficients of the polynomial for α, n, Q and lnA in the osA-type equation. After determining material parameters under different strains, the predicted stress can be determined based on Equation (17), which is shown in Figure 7.…”

“…To predict the hot flow stress of 10% Cr steel alloy, Abdallah et al [15] found the prediction accuracy of the strain-compensated Arrhenius-type model is higher than that of modified Johnson-Cook. Moreover, the previous comparative studies have found the strain-compensated Arrhenius-type equation can give a more accurate description of the relationship between flow stress and deformation conditions when compared with the modified Johnson-Cook, Johnson-Cook equations [16,17]. Moreover, based on the stress-strain data of T24 steel, a comparative study of Li et al [18] showed the predictability of the strain-compensated Arrhenius-type equation is stronger than that of the modified Zerilli-Armstrong equation.…”

A comparative analysis of several material models in LS-DYNA at high velocity impact

“…In order to determine the parameter 1 H numerical simulation of impact of the obstacle at high-speed collision with the steel impactor was carried out using finite element package LS-DYNA [5]. LS-DYNA has a wide range of options for describing the behavior of elastoplastic materials under dynamic loads, but the choice of constants for a particular material is not an easy task, as it might seem at first glance [6][7][8].The problem was solved for 3D case in the Lagrangian formulation. Geometric model (see Fig.…”

Data-Driven Construction Method of Material Mechanical Behavior Model

“…Conducting experiments is the most direct and effective way to obtain the mechanical behavior of materials. Scholars have constructed material constitutive models [1][2][3] using experiments to describe the stress-strain response of materials, and then study the mechanical properties of materials. Xin, X.…”