A Flow Stress Model of 300M Steel for Isothermal Tension

Chen, Rongchuang; Zhang, Shiyang; Liu, Xianlong; Feng, Fan

doi:10.3390/ma14020252

Cited by 9 publications

(9 citation statements)

References 25 publications

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“…The average percentage error was 2.43%, and the average absolute error was 3.59 MPa. The proposed model showed an advantage in precision compared with the modified Arrhenius model (6.23%, 6.81 MPa) and the Kocks-Mecking model (6.59%, 5.37 MPa) in previous literatures [28,32]. low compared with the error at high stress levels, because the average percentage error was used in error evaluation.…”

Section: Application Of the Modelmentioning

confidence: 85%

“…It could be seen that the average grain size model was accurate. The confidence level [28] evaluating the accuracy of the model, R, was calculated to be 0.97, showing the model precision was acceptable. The average error was 3.84% (2.04 μm).…”

Section: Ln(mentioning

confidence: 99%

“…Chen et al [27] carried out multi-pass compression tests and established a constitutive model by considering the microstructure evolution processes such as DRX, sub-dynamic recrystallization and SRX. Besides, the authors developed a constitutive model for 300M steel under tension based on the Arrhenius model [28].…”

Section: Introductionmentioning

confidence: 99%

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Unified Modelling of Flow Stress and Microstructural Evolution of 300M Steel under Isothermal Tension

Chen

Zhang

Wang

et al. 2021

Metals

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Constitutive models that reflect the microstructure evolution is of great significance to accurately predict the forming process of forging. Through thermal tension of 300M steel under various temperatures (950~1150 °C) and strain rates (0.01~10 s−1), the material flow and microstructure evolutions were investigated. In order to describe both the exponential hardening phenomenon at a higher temperature, and the softening phenomenon due to recrystallization at a lower temperature, a constitutive model considering microstructure evolution was proposed based on the Kocks–Mecking model. It was found that considering the stress-strain curve to be exponential in the work-hardening stage could improve the constitutive model prediction precision. The average error was 2.43% (3.59 MPa), showing that the proposed model was more precise than the modified Arrhenius model and the Kocks–Mecking model. The models to describe recrystallization kinetics and average grain size were also constructed. This work enabled the Kocks–Mecking model to predict stress-strain curves with a higher accuracy, and broadened the applicable range of the Kocks–Mecking model.

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Section: Application Of the Modelmentioning

confidence: 85%

Section: Ln(mentioning

confidence: 99%

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Unified Modelling of Flow Stress and Microstructural Evolution of 300M Steel under Isothermal Tension

Chen

Zhang

Wang

et al. 2021

Metals

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“…In most cases, the equation accuracy will increase with the increase in the number of test sets [ 11 , 12 ]. Still, a larger deviation can be seen under the condition of high stress [ 12 , 13 ]. Additionally, optimization works related to the Arrhenius equation were also conducted in in-depth research.…”

Section: Introductionmentioning

confidence: 99%

Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model

Chen²,

Bhandari³

et al. 2022

Materials

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To realize the purpose of energy saving, materials with high weight are replaced by low-weight materials with eligible mechanical properties in all kinds of fields. Therefore, conducting research works on lightweight materials under specified work conditions is extremely important and profound. To understand the relationship of aluminum alloy AA5005 among flow stress, true strain, strain rate, and deformation temperature, hot isothermal tensile tests were conducted within the strain rate range 0.0003–0.03 s−1 and temperature range 633–773 K. Based on the true stress-true strain curves obtained from the experiment, a traditional constitutive regression Arrhenius-type equation was utilized to regress flow behaviors. Meanwhile, the Arrhenius-type equation was optimized by a sixth-order polynomial function for compensating strain. Thereafter, a back propagation artificial neural network (BP-ANN) model based on supervised machine learning was also employed to regress and predict flow stress in diverse deform conditions. Ultimately, by introducing statistical analyses correlation coefficient (R2), average absolute relative error (AARE), and relative error (δ) to the comparative study, it was found that the Arrhenius-type equation will lose accuracy in cases of high stress. Additionally, owning higher R2, lower AARE, and more concentrative δ value distribution, the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation.

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“…The 7075 aluminum alloy is the earliest ultra-high strength aluminum alloy and is mainly used for the loadbearing parts of aircraft [1]. Similar to most alloys [4][5][6], the 7000 series aluminum alloys are generally produced by various thermoplastic processes, including extrusion, forging and rolling. However, their thermoplastic forming is complicated, because the deformation behavior is controlled by deformation conditions including strain, strain rate and temperature [7,8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Flow Stress of Annealed 7075 Al Alloy in Hot Deformation Using Strain-Compensated Arrhenius and Neural Network Models

Yang

et al. 2021

Materials

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Hot compression experiments of annealed 7075 Al alloy were performed on TA DIL805D at different temperatures (733, 693, 653, 613 and 573 K) with different strain rates (1.0, 0.1, 0.01 and 0.001 s−1.) Based on experimental data, the strain-compensated Arrhenius model (SCAM) and the back-propagation artificial neural network model (BP-ANN) were constructed for the prediction of the flow stress. The predictive power of the two models was estimated by residual analysis, correlation coefficient (R) and average absolute relative error (AARE). The results reveal that the deformation parameters including strain, strain rate, and temperature have a significant effect on the flow stress of the alloy. Compared with the SCAM model, the flow stress predicted by the BP-ANN model is in better agreement with experimental values. For the BP-ANN model, the maximum residual is only 1 MPa, while it is as high as 8 MPa for the SCAM model. The R and AARE for the SCAM model are 0.9967 and 3.26%, while their values for the BP-ANN model are 0.99998 and 0.18%, respectively. All these reflect that the BP-ANN model has more accurate prediction ability than the SCAM model, which can be applied to predict the flow stress of the alloy under high temperature deformation.

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A Flow Stress Model of 300M Steel for Isothermal Tension

Cited by 9 publications

References 25 publications

Unified Modelling of Flow Stress and Microstructural Evolution of 300M Steel under Isothermal Tension

Unified Modelling of Flow Stress and Microstructural Evolution of 300M Steel under Isothermal Tension

Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model

Prediction of Flow Stress of Annealed 7075 Al Alloy in Hot Deformation Using Strain-Compensated Arrhenius and Neural Network Models

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