Modeling the Flow Behavior of a High‐Manganese Steel Fe‐Mn23‐C0.6 in Consideration of Dynamic Recrystallization

Xiong, Wei; Wietbrock, Burkhard; Saeed-Akbari, Alireza; Bambach�, Markus

doi:10.1002/srin.201000263

Cited by 21 publications

(7 citation statements)

References 46 publications

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“…7c, the value of activation energy Q changed in the range of 347.5-370.0 kJ/mol, with a mean value of * 358.5 kJ/mol. Compared to the reported values for hot deformation of high-Mn steels such as 387.84 kJ/mol in Fe-20Mn-3Si-3Al [38] and 439 kJ/mol in Fe-23Mn-0.6C [39], the obtained values are significantly lower. This difference in activation energy has been suggested to relate to the high solute content present in high-Mn steel [40].…”

Section: Constitutive Modeling Flow Curvescontrasting

confidence: 80%

Hot Deformation Behavior and Processing Maps of a Medium Manganese TRIP Steel

Yan

Huang

et al. 2018

Acta Metall. Sin. (Engl. Lett.)

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The hot deformation behavior of a medium-Mn steel was studied in terms of hot compression flow curves in the temperature range of 850-1050°C and strain rates of 0.05-10 s-1. The thermo-mechanical analysis was carried out and suggested that the microstructure during deformation was completely austenite which had high tendency for dynamic recrystallization (DRX). The flow behavior was characterized by significant flow softening at deformation temperatures of 950-1050°C and lower strain rates of 0.05-5 s-1 , which was attributed to heating during deformation, DRX and flow instability. A step-by-step calculating procedure for constitutive equations is proposed. The verification of the modified equations indicated that the developed constitutive models could accurately describe the flow softening behavior of studied steel. Additionally, according to the processing maps and microstructure analysis, it suggested that hot working of medium-Mn steel should be carried out at 1050°C, and the strain rate of 0.05-10 s-1 resulted in significantly recrystallized microstructures in the in steel. The flow localization is mainly flow instability mechanism for experimental steel.

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Section: Constitutive Modeling Flow Curvescontrasting

confidence: 80%

Hot Deformation Behavior and Processing Maps of a Medium Manganese TRIP Steel

Yan

Huang

et al. 2018

Acta Metall. Sin. (Engl. Lett.)

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“…The ANN model was constructed using the data from scientific papers devoted to the hot deformation behavior of high-Mn light-weight steels [ 19 , 20 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. The database consists of the values of input variables: alloying element content (C, Si, Mn, Al) and thermomechanical parameters (strain rate, temperature, strain), and an output property (true stress).…”

Section: Methodsmentioning

confidence: 99%

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Churyumov

Kazakova

2023

Materials

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The development of new lightweight materials is required for the automotive industry to reduce the impact of carbon dioxide emissions on the environment. The lightweight, high-manganese steels are the prospective alloys for this purpose. Hot deformation is one of the stages of the production of steel. Hot deformation behavior is mainly determined by chemical composition and thermomechanical parameters. In the paper, an artificial neural network (ANN) model with high accuracy was constructed to describe the high Mn steel deformation behavior in dependence on the concentration of the alloying elements (C, Mn, Si, and Al), the deformation temperature, the strain rate, and the strain. The approval compression tests of the Fe–28Mn–8Al–1C were made at temperatures of 900–1150 °C and strain rates of 0.1–10 s−1 with an application of the Gleeble 3800 thermomechanical simulator. The ANN-based model showed high accuracy, and the low average relative error of calculation for both training (5.4%) and verification (7.5%) datasets supports the high accuracy of the built model. The hot deformation effective activation energy values for predicted (401 ± 5 kJ/mol) and experimental data (385 ± 22 kJ/mol) are in satisfactory accordance, which allows applying the model for the hot deformation analysis of the high-Mn steels with different concentrations of the main alloying elements.

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“…Materials and experimental procedure. In this study, a recently developed high-manganese steel (HMS) is analyzed for the onset of DRX [4]. It is highly important to characterize the material properties of newly developed steels, especially DRX, to set up roll pass schedules for hot rolling.…”

Section: Methodsmentioning

confidence: 99%

Influence of Different Interpolation Techniques on the Determination of the Critical Conditions for the Onset of Dynamic Recrystallisation

Lohmar

Bambach�

2013

MSF

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Accurate modeling of dynamic recrystallization (DRX) is highly important for forming processes like hot rolling and forging. To correctly predict the overall level of dynamic recrystallization reached, it is vital to determine and model the critical conditions that mark the start of DRX. For the determination of the critical conditions, a criterion has been proposed by Poliak and Jonas. It states that the onset of DRX can be detected from an inflection point in the work hardening rate as a function of flow stress. The work hardening rate is the derivative of the flow stress with respect to strain. Flow curves are in general measured at a certain sampling rate, yielding tabular stress-strain data, which are per se not continuously differentiable. In addition, inevitable jitter occurs in measured flow curves. Hence, flow curves need to be interpolated and smoothed before the work hardening rate and further derivatives necessary for evaluating the criterion by Poliak and Jonas can be computed. In this paper, the polynomial interpolation originally proposed by Poliak and Jonas is compared to a new approach based on radial basis functions using a thin plate spline kernel, which combines surface interpolation of various flow curves and smoothing in a single step. It is shown for different steel grades that the interpolation method used has a crucial influence on the resulting critical conditions for DRX, and that a simultaneous evaluation by surface interpolation might yield consistent critical conditions over a range of testing temperatures.

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Modeling the Flow Behavior of a High‐Manganese Steel Fe‐Mn23‐C0.6 in Consideration of Dynamic Recrystallization

Cited by 21 publications

References 46 publications

Hot Deformation Behavior and Processing Maps of a Medium Manganese TRIP Steel

Hot Deformation Behavior and Processing Maps of a Medium Manganese TRIP Steel

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Influence of Different Interpolation Techniques on the Determination of the Critical Conditions for the Onset of Dynamic Recrystallisation

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