Numerical simulation of dynamic strain-induced austenite–ferrite transformation in a low carbon steel

Zheng, Chengwu; Xiao, Namin; Hao, Likai; Li, Dianzhong; Li, Yiyi

doi:10.1016/j.actamat.2009.03.005

Cited by 62 publications

(55 citation statements)

References 40 publications

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“…11, 12), although the average prior-austenite grain size was larger than that during holding at 1100°C. It has been observed that a finer prior-austenite grain size before deformation increases the extent of DIF [37]. In the present study, in contrast to the observations of [13,17,[38][39][40][41][42][43][44][45][46], DIF was promoted by a larger prioraustenite grain size, arising from the reheating process between first and final deformation.…”

Section: Simulation Of Texturecontrasting

confidence: 56%

The effect of thermomechanical controlled processing on recrystallisation and subsequent deformation-induced ferrite transformation textures in microalloyed steels

et al. 2018

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The evolution of texture components for two experimental 0.06 wt% C steels, one containing 0.03 wt% Nb (Nb steel) and the second containing both 0.03 wt% Nb and 0.02 wt% Ti (Nb-Ti steel), was investigated following a new thermomechanical controlled process route, comprising first deformation, rapid reheat to 1200°C and final deformation to various strains. Typical deformation textures were observed after first deformation for both steels. Following subsequent reheating to 1200°C for various times, the recrystallisation textures consisted primarily of the a-011 h i//RD texture fibre with a weak c-{111}//ND texture fibre, similar to deformation textures, indicative of the dominance of a strain-induced boundary migration mechanism. The texture components after finish deformation were different from the rough deformation textures, with a strong a-011 h i//RD texture fibre at the beginning, and then the strong peaks move to (111) 1 21 and (111) 1 12 textures due to the deformation-induced ferrite (DIF) transformation. The effect of Ti on the recrystallisation textures and deformation textures has also been analysed in this study. The results illustrate that Ti significantly influences the c-{111}//ND texture fibre. Finally, the textures after deformation and recrystallisation in the austenite were calculated based on the K-S orientation relationship between the austenite and ferrite. This allowed the understanding of the mechanism of recrystallisation between first and final deformation and the DIF textures during phase transformation.

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Section: Simulation Of Texturecontrasting

confidence: 56%

The effect of thermomechanical controlled processing on recrystallisation and subsequent deformation-induced ferrite transformation textures in microalloyed steels

et al. 2018

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“…These regression models are not accurate enough and can only be used over a small range, which latter depends on the experimental data. Recently, Zheng et al developed an integrated CA model to simulate dynamic strain-induced transformation (DSIT) in low-carbon steels [100,101]. Following that, a modified CA model was developed to simulate the microstructural evolution during post-dynamic transformation (post-DT) after DSIT.…”

Section: Ca Model For Phase Transformationsmentioning

confidence: 99%

“…These features indicate that it is possible to model the microstructure evolution within a unified frame [64][65][66]. In contrast to the limited applicability of the macro-scale models, e.g., the phenomenological model and the statistical model, this characteristic of mesoscopic models also shows the latent advantage of the numerical solution of the complexity of microstructural evolution globally , such as normal grain growth [68][69][70][71][72][73][74][75][76], recrystallization [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98] and phase transformation [100][101][102][103]. 4.…”

Section: Introductionmentioning

confidence: 99%

Prediction of microstructural evolution during hot forging

2014

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-Microstructural evolution, which is governed by temperature, strain and strain rate during hot forging, is a key factor influencing mechanical properties. Understanding the microstructural evolution of metals and alloys in hot forging has a great importance for the designers of metal forming processes. The principal objective of this paper is to provide an overview of the models for the prediction of microstructural evolution for metals and alloys during the hot forging process. In this review paper, the models are divided into four categories, including the phenomenological, physically-based, mesoscale and artificial-neural-network models, to introduce their developments, prediction capabilities and application scopes. Additionally, some limitations and objective suggestions for the further development of the modelling of microstructural evolution during hot forging are proposed.

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“…In order to determine the input parameters for the MPF model, we performed a preliminary simulation of the DIFT in a Fe-0.15wt.%C alloy at a temperature of 750°C and strain rate of 0.1 s -1 ; these conditions are same as those employed by Choi et al 32) Then, the parameters to be used for the subsequent MPF simulation were identified by comparing the simulated flow stress curve with the experimentally determined one. The physical values and input parameters obtained using the above-mentioned procedure are shown as follows: [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] For Eqs. (3) and (4) .…”

Section: Simulation Condition and Proceduresmentioning

confidence: 99%

“…Recently, Zheng et al proposed a cellular automaton (CA) model and used it to simulate the DIFT as well as the DRX of the ferrite phase. 11,12) However, it has been pointed out that using CA and MC models leads to problems when modeling, on the absolute time scale, the curvature-driven growth of the grain boundaries. 13) These problems are serious shortcomings when it comes to quantitatively simulating the shrinking and coarsening of the ferrite grains formed by the DIFT and DRX.…”

Section: Introductionmentioning

confidence: 99%

Multi-Phase-Field Simulation of Flow Stress and Microstructural Evolution during Deformation-Induced Ferrite Transformation in a Fe–C Alloy

Yamanaka

Takaki

2014

ISIJ Int.

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Deformation-induced ferrite transformation (DIFT) is one of the most effective ways of refining ferrite grains in steel. In this study, we employed a multi-phase-field (MPF) model to simulate both variations in macroscopic flow stress and microstructural evolution during DIFT. Using the MPF model, two-dimensional simulations of DIFT in a Fe-C alloy were performed to investigate the effects of strain rate, austenite grain size, and dynamic recrystallization (DRX) of the ferrite phase on flow stress curve and ferrite grain size. The results demonstrated that increasing the rate of ferrite nucleation by increasing the strain rate and reducing the austenite grain size is essential to obtaining fine-grained ferrite. The results of the simulations also indicated that it is important to reduce the interfacial mobility and increase the nucleation rate of the ferrite grains subjected to DRX in order to obtain ultrafine-grained ferrite by DIFT when it is accompanied by DRX of the ferrite phase. Thus, the MPF model is an effective tool for elucidating the correlation between the variation in the flow stress and the evolution of the ferrite grains during DIFT.

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Numerical simulation of dynamic strain-induced austenite–ferrite transformation in a low carbon steel

Cited by 62 publications

References 40 publications

The effect of thermomechanical controlled processing on recrystallisation and subsequent deformation-induced ferrite transformation textures in microalloyed steels

The effect of thermomechanical controlled processing on recrystallisation and subsequent deformation-induced ferrite transformation textures in microalloyed steels

Prediction of microstructural evolution during hot forging

Multi-Phase-Field Simulation of Flow Stress and Microstructural Evolution during Deformation-Induced Ferrite Transformation in a Fe–C Alloy

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