The Flow Stress Behavior and Constitutive Model of Cr8Mo2SiV Tool Steel during Hot Deformation

Li, Changsheng; He, Shuai; Ren, Jia; Han, Yahui

doi:10.1002/srin.202000434

Cited by 11 publications

(6 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, the accuracy of different constitutive models is further quantitatively analyzed. The Pearson correlation coefficient is used in this study, which takes the form shown in Equation (24).…”

Section: Prediction Accuracy Comparison Of Different Constitutive Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Flow Stress Characteristics and Constitutive Modeling of Typical Ultrahigh‐Strength Steel under High Temperature and Large Strain

Zhao

Huang

et al. 2022

steel research int.

View full text Add to dashboard Cite

Generally, steels with a yield strength more than 1400 MPa are defined as ultrahigh-strength steels. [1] Ultrahigh-strength steels own exceptional mechanical qualities and are utilized to create large key load-bearing components, [2][3][4] which are commonly treated by hot forging with considerable strain. During the hot forging process with large strain, the mechanisms of dynamic recovery and dynamic recrystallization occur, which make it difficult to control the flow behaviors. [5,6] In addition, flow behaviors closely depend on strain, strain rate, and deformation temperature during the hot deformation process. The flow characteristics are dramatically different at various deformation parameters. The numerous features make it challenging to forecast flow characteristics and optimize hot processing parameters. [7] Constitutive modeling is a powerful method to simulate the real deformation process. [8] So, it is vital to analyze flow characteristics and develop an appropriate constitutive model for ultrahigh-strength steels during the hot forging process with large strain.Constitutive models include artificial neural network models, physical-based constitutive models, and phenomenological constitutive models. [9,10] Artificial neural network models can accurately capture the intricate link between flow characteristics and multiple deformation factors. Wan et al. [11] utilized the methods of back propagation neural network and particle swarm optimization to predict the flow behaviors of Zr-4 alloy during hot deformation, and the correlation coefficient between predicted and experimental flow stress is 0.9981. Li et al. [12] employed a deep neural network to characterize the flow characteristics of Ti-2Al-9.2Mo-2Fe beta titanium alloy during hot deformation. The prediction accuracy of the constitutive model is 0.9995. Murugesan et al. [13] explored the accuracy of different supervised machine learning methods in predicting flow stress and pointed out that the random forest regression model has the highest accuracy. The flow behaviors can be correctly predicted by artificial neural network models. However, as stated by Savaedi et al., [14] the successful application of artificial neural network models depends on high-quality data. Furthermore, compared to the computation efficiency of analytical models, the computation efficiency of artificial neural network models is lower. [15] Physical-based constitutive models may express the physical meanings of material during the deformation process. Buzolin et al. [16] constructed a dislocation-based constitutive model to predict the flow behaviors and microstructure evolution during the hot deformation process of Ti5553 alloy. Zeng et al. [17] built a unified constitutive model

show abstract

Section: Prediction Accuracy Comparison Of Different Constitutive Modelsmentioning

confidence: 99%

“…There are numerous researches on phenomenological constitutive models for magnesium alloys, [20,21] aluminum alloys, [22,23] steels, [24][25][26][27] titanium alloys, [28][29][30] superalloy, [31][32][33] etc. Long et al [34] established an Arrhenius constitutive model to characterize the flow characteristics of ZK61 magnesium alloy.…”

mentioning

confidence: 99%

Flow Stress Characteristics and Constitutive Modeling of Typical Ultrahigh‐Strength Steel under High Temperature and Large Strain

Zhao

Huang

et al. 2022

steel research int.

View full text Add to dashboard Cite

show abstract

“…It describes the dynamic response of material stress to temperature, deformation rate, strain, and other process parameters under specific conditions. The Arrhenius-type models [5] are widely used in TMP, [6][7][8] owing to their simple form, easy calibration of parameters, and the ability to relatively accurately track the deformation behavior of various materials [9][10][11] throughout the entire thermomechanical process. The Arrhenius equation can be used to determine the activation energy of hot deformation [12] in relation to microstructure evolution as well.…”

Section: Introductionmentioning

confidence: 99%

A Modified Arrhenius‐Type Constitutive Model and its Implementation by Means of the Safe Version of Newton–Raphson Method

Liang

Kong

Zhang

et al. 2022

steel research int.

View full text Add to dashboard Cite

Arrhenius‐type constitutive models are widely used in thermomechanical processing due to their ease of parameter calibration and ability to track the deformation behavior with various materials. Herein, a modified Arrhenius model is proposed, and the model is implemented in the finite element (FE) simulation to improve the simulation accuracy. The VUMAT Fortran subroutine of the model is established for the commercial nonlinear FE software Abaqus/Explicit based on an implicit time integration scheme and the radial return mapping algorithms. A root‐finding method called the safe version of Newton–Raphson method is applied to calculate the plastic corrector term. The efﬁciency and accuracy of both analytical and numerical methods are evaluated for calculating the derivatives in this algorithm. The implementation of the user‐defined modified Arrhenius‐type material model is successfully applied to a thermal–mechanical coupling FE model for the hot rolling of heavy steel plate. The goal optimization function is used to calibrate the material parameters for the model. The plastic strain field and temperature field are in accordance with the theoretical derivation and experimental results. It provides valuable references for characterizing and optimizing thermomechanical processing.

show abstract

“…However, for materials whose rheological behavior is significantly affected by strain, temperature, and strain rate, since the Arrhenius-type model does not consider the influence of temperature, strain rate, and strain on model parameters, the prediction accuracy of the model for this kind of materials is not high. Through research, Li [ 10 ] and Safar [ 11 ] pointed out that the modified Arrhenius-type constitutive model considering the influence of plastic strain on model parameters could accurately predict the rheological behavior of steel under high temperature deformation. Therefore, the modified Arrhenius-type constitutive model, which can reflect the relationship between the model parameters and temperature, strain rate, and strain, is widely used to evaluate the interaction between the softening effect of dynamic recrystallization and the hardening effect of deformation under thermal deformation [ 5 , 12 ] The Johnson–Cook model is another phenomenological constitutive equation and is used to describe the rheological behavior of materials under high temperature deformation [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Deformation Behavior and Constitutive Model of 34CrNi3Mo during Thermo-Mechanical Deformation Process

Jia

Zhou

2022

Materials

View full text Add to dashboard Cite

With higher creep strength and heat resistance, 34CrNi3Mo has been widely used in the production of engine rotors, steam turbine impellers, and turbine blades. To investigate the hot deformation behaviors of 34CrNi3Mo steel, hot compressive tests were conducted on a Gleeble-3500 thermomechanical simulator, under the temperature range of 1073 K–1373 K and strain rate ranges of 0.1 s−1–20 s−1. The results show that the flow stress of 34CrNi3Mo steel under high temperatures is greatly influenced by the deformation temperature and strain rate, and it is the result of the interaction between strain hardening, dynamic recovery, and recrystallization. Under the same deformation rate, as the deformation temperature increases, the softening effect of dynamic recrystallization and dynamic recovery gradually increases, and the flow stress gradually decreases. Under the same deformation temperature, with the increase of strain rate, the influence of strain hardening on 34CrNi3Mo steel is gradually in power, and the flow stress gradually increases. To predict the flow stress of 34CrNi3Mo steel accurately, a modified Arrhenius-type constitutive model considering the effects of strain, temperature, and strain rate at the same time was made based on the experiment data. On this basis, the evolution law of deformation activation and instability characteristics of 34CrNi3Mo steel were investigated, and the processing map of 34CrNi3Mo steel was established. The formability of 34CrNi3Mo steel under high temperature deformation was revealed, which provided a theoretical foundation of the equation of reasonable hot working process.

show abstract

The Flow Stress Behavior and Constitutive Model of Cr8Mo2SiV Tool Steel during Hot Deformation

Cited by 11 publications

References 52 publications

Flow Stress Characteristics and Constitutive Modeling of Typical Ultrahigh‐Strength Steel under High Temperature and Large Strain

Flow Stress Characteristics and Constitutive Modeling of Typical Ultrahigh‐Strength Steel under High Temperature and Large Strain

A Modified Arrhenius‐Type Constitutive Model and its Implementation by Means of the Safe Version of Newton–Raphson Method

Deformation Behavior and Constitutive Model of 34CrNi3Mo during Thermo-Mechanical Deformation Process

Contact Info

Product

Resources

About