Comparison of Five Different Models Predicting the Hot Deformation Behavior of EA4T Steel

Bai, Jie; Huo, Yuanming; He, Tao; Bian, Zhi-Yuan; Ren, Xu; Du, Xiaoxu

doi:10.1007/s11665-022-06828-y

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…Therefore, constructing the L-J constitutive model of EA4T steel before creating the CA model is very helpful. Many studies have focused on various constitutive models of EA4T steel, [21,25] but an L-J constitutive model of EA4T steel is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel

Ren

Huo

et al. 2023

steel research int.

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Herein, isothermal compression experiments are conducted on EA4T steel at 970–1170 °C, with strain rates of 0.01–1.0 s−1 and a strain of 0.2–0.8 s−1. Based on the experimental data, a high‐temperature constitutive model is developed for EA4T steel. The activation energy of dynamic recrystallization (DRX) is calculated to be 383 666 J mol−1, and the correlation coefficient and root mean square error between the results of the constitutive model and experimental results are 0.9943 and 4.6823, respectively. The average grain size for each deformation condition is determined using the linear‐intercept method. The grain growth model widely used in cellular automaton (CA) simulations is found unsuitable for EA4T steel. Therefore, a modified CA model of DRX behavior suitable for EA4T steel is developed. The nucleation rates and solute drag effect coefficients under different deformation conditions are determined. Furthermore, simulations are performed under other deformation conditions using the CA model. The simulated results for the average grain size, microstructure morphology, and DRX fraction agree well with the experimental results. The reason for the deviation between the observed and simulated DRX fractions is also explored.

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Section: Introductionmentioning

confidence: 99%

Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel

Ren

Huo

et al. 2023

steel research int.

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“…Despite their simplicity, each of them suffers from certain shortcomings: as reported by Jia et al [20], the JC model suffers from a lack of non-coupling between the strain, the strain rate, and the temperature, while the HS model is better adapted for higher strain rate conditions [18]. To circumvent these shortcomings, several modified model forms have been developed [18,[20][21][22][23][24][25]. However, as reported by many authors [26][27][28], even after adjusting the constants, the high-temperature flow behavior, particularly when dynamic recrystallization takes place, cannot be accurately predicted, and none of the models is able to accurately predict the flow behavior outside the experimental testing interval that was used to determine the model constants.…”

Section: Introductionmentioning

confidence: 99%

Interpolation and Extrapolation Performance Measurement of Analytical and ANN-Based Flow Laws for Hot Deformation Behavior of Medium Carbon Steel

Mha

Dhondapure

Jahazi

et al. 2023

Metals

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In the present work, a critical analysis of the most-commonly used analytical models and recently introduced ANN-based models was performed to evaluate their predictive accuracy within and outside the experimental interval used to generate them. The high-temperature deformation behavior of a medium carbon steel was studied over a wide range of strains, strain rates, and temperatures using hot compression tests on a Gleeble-3800. The experimental flow curves were modeled using the Johnson–Cook, Modified-Zerilli–Armstrong, Hansel–Spittel, Arrhenius, and PTM models, as well as an ANN model. The mean absolute relative error and root-mean-squared error values were used to quantify the predictive accuracy of the models analyzed. The results indicated that the Johnson–Cook and Modified-Zerilli–Armstrong models had a significant error, while the Hansel–Spittel, PTM, and Arrhenius models were able to predict the behavior of this alloy. The ANN model showed excellent agreement between the predicted and experimental flow curves, with an error of less than 0.62%. To validate the performance, the ability to interpolate and extrapolate the experimental data was also tested. The Hansel–Spittel, PTM, and Arrhenius models showed good interpolation and extrapolation capabilities. However, the ANN model was the most-powerful of all the models.

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A High-Temperature Strain-Compensated Arrhenius-Type Constitutive Model and an Improved Avrami-Type Dynamic Recrystallization Model of 40CrNiMo

Yuan-ming,

Cun-long,

Tao

et al. 2023

J. of Materi Eng and Perform

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Comparison of Five Different Models Predicting the Hot Deformation Behavior of EA4T Steel

Cited by 6 publications

References 40 publications

Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel

Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel

Interpolation and Extrapolation Performance Measurement of Analytical and ANN-Based Flow Laws for Hot Deformation Behavior of Medium Carbon Steel

A High-Temperature Strain-Compensated Arrhenius-Type Constitutive Model and an Improved Avrami-Type Dynamic Recrystallization Model of 40CrNiMo

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