Effects of composition and strain rate on hot ductility of Cr–Mo-alloy steel in the two-phase region

Zheng, Yaxu; Shen, Wei; Zhu, Lingkai; Guo, Zhihong; Wang, Qi; Feng, Jie; Li, Yongliang; Cao, Ruifang; Wu, Jiayi

doi:10.1515/htmp-2021-0025

Cited by 9 publications

(15 citation statements)

References 24 publications

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“…Copper influences more intensively on the stress and may change its value on 90 MPa at a deformation temperature of 1150 • C. A similar result was previously obtained experimentally by Li et al [65]: adding copper to 304L stainless steel significantly decreased the true stress due to increasing the stacking fault energy and, consequently, inhibiting the dynamic recrystallisation. Previously, the significant influence of the minor variations of the 25CrMo steel chemical composition on the hot fracture behaviour was shown by Zheng et al [66]. The changes in the mechanical properties of the hot-deformed low-carbon microalloyed steels with the change of the chemical composition within the steel grade were also shown in [67].…”

Section: Discussionmentioning

confidence: 88%

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Churyumov

Kazakova

Churyumova

2022

Metals

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Hot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composition limits their application. In this paper, a novel artificial neural network (ANN) model was built to determine the steel flow stress with high accuracy in the wide range of the concentration of the elements in high-alloyed, corrosion-resistant steels. The additional compression tests for stainless Cr12Ni3Cu steel were carried out at the strain rates of 0.1–10 s−1 and the temperatures of 900–1200 °C using thermomechanical simulator Gleeble 3800. The ANN-based model showed high accuracy for both training (the error was 6.6%) and approvement (11.5%) datasets. The values of the effective activation energy for experimental (410 ± 16 kJ/mol) and predicted peak stress values (380 ± 29 kJ/mol) are in good agreement. The implementation of the constructed ANN-based model showed a significant influence of the Cr12Ni3Cu chemical composition variation within the grade on the flow stress at a steady state of the hot deformation.

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Section: Discussionmentioning

confidence: 88%

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Churyumov

Kazakova

Churyumova

2022

Metals

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“…They reported that Nb reduces the hot crack resistance in the studied steel grades, while Ti improves the hot ductility. Zheng et al showed that Nb reduces the hot ductility when increasing the temperature from 650 °C to 850 °C [6]. Moreover, they reported that the addition of B improves the hot ductility in Nb-containing steels.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, Nb-containing micro-alloyed steels show lower hot ductility due to a higher amount of ferrite films at grain boundaries than those containing B. However, the hot ductility of Nb-B steels is still lower than that of non-alloyed steels [6]. Gontijo et al studied the influence of precipitates and ferrite formation kinetics at grain boundaries on the hot ductility of micro-alloyed steels [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling the hot deformation behavior of micro-alloyed steel in the single and two-phase fields

Sharifi,

Buzolin,

Bakhtiari

et al. 2023

J. Phys.: Conf. Ser.

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Continuous casting is a well-established process to produce steel slabs. However, the surfaces of the slabs may crack during processing. The occurrence of cracks is related to different phenomena, i.e., ferrite formation, presence of precipitates, and microstructural modifications during manufacturing processing. In the present work, we developed a physically based mesoscale model to describe the plastic deformation of micro-alloyed steel in the single and two-phase fields, as well as the microstructure evolution. We implemented a dislocation density-based constitutive model to calculate the strain hardening and the stress softening produced by dynamic recovery using the Kocks-Mecking (KM) formalism for the austenite and ferrite. We coupled the KM model with the Johnson-Mehl-Avrami-Kolmogorov model to consider the dynamic recrystallization of the austenitic phase. The nucleation and growth of the recrystallized grains, driven by the stored energy, compete with the annihilation of dislocations due to dynamic recovery. In the two-phase domain, the iso-work condition for the load partition is implemented. We calculated the ferrite volume fraction by fitting the data obtained using JmatPro software. Moreover, an Arrhenius-type equation correlates the yield stress to the Zener-Hollomon parameter. We validated the model with isothermal uniaxial compression tests of microalloyed steel over the temperature range of 650 °C-1100 °C and strain rates of 10−2 s−1 and 10−3 s−1 using a Gleeble® 3800 thermomechanical simulator.

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“…The high-temperature mechanical properties of the steel are dependent not only on temperature, strain rate, and strain but primarily on the chemical composition. This influence may be significant even within the same steel grade [6][7][8]. Currently, a large number of constitutive models that connect the stress and thermomechanical parameters such as temperature, strain rate, and strain were developed [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development of computational system based on artificial neural network for prediction of high temperature deformation behaviour in steels

Churyumov¹

2022

cisisr

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A significant number of the data about the hot deformation behaviour of the metallic materials is published up to date in the scientific sources. As a result, the systematization and formalization of such big datasets using mathematical modeling is required. However, the accuracy of the usual regression models is not enough for this purpose. The artificial neural network (ANN) based model for prediction of the steel flow stress under the hot deformation conditions was constructed. The model has shown a high accuracy on the training dataset such as on the independent additional compression tests of the stainless 13Cr11Ni2W2MoV steel. The compression tests were carried out in the strain rate range of 0.1, 1, and 10 s -1 and the temperature range of 1000 -1200 ºC using thermomechanical simulator Gleeble 3800. The steel has a two-phase ferritic/austenitic microstructure in the hot deformation range. The average relative errors for the training dataset and approvement tests were about 8.8 and 9.5 %, respectively. The constructed model may be used for the determination of the different element influence the flow stress of the steel, which may allow fast correction of the hot deformation conditions. Special software was developed for the use of the built ANN-based model.

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Effects of composition and strain rate on hot ductility of Cr–Mo-alloy steel in the two-phase region

Cited by 9 publications

References 24 publications

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Modeling the hot deformation behavior of micro-alloyed steel in the single and two-phase fields

Development of computational system based on artificial neural network for prediction of high temperature deformation behaviour in steels

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