Mesoscale simulation of microstructure evolution during multi-stage hot forging processes

Chen, Fei; Cui, Zhenshan

doi:10.1088/0965-0393/20/4/045008

Cited by 26 publications

(16 citation statements)

References 49 publications

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“…Generally speaking, the CA framework for recrystallization has the similar form which is composed of the following parts [79,93]:…”

Section: Implementation Of Recrystallization In the Ca Modelmentioning

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%

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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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“…Generally speaking, the CA framework for recrystallization has the similar form which is composed of the following parts [79,93]:…”

Section: Implementation Of Recrystallization In the Ca Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of microstructural evolution during hot forging

2014

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“…The reason for such a difference may be because that the elongated grain boundaries provide more potential nucleation sites for recrystallization due to a higher ratio of the grain boundary volume to grain volume. At the same time, the newly formed recrystallized grains impinge earlier with support of the morphology change of the matrix, resulting in a smaller average grain size as compared to the conventional CA simulation [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In addition, it can be also found that the grain morphology deformation has a decreased effect on the critical strain for DRX.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…(4) can be determined for a deforming material. Second, the constant C dynamic can be determined using an inverse analysis method [9,14]. The dislocation density of recrystallized grain evolves from zero to a saturated value.…”

Section: Modeling Of Nucleation and Grain Growthmentioning

confidence: 99%

“…Ding et al [12] investigated the hot compression behavior of the Mg-9Al-1Zn alloy using EBSP analysis and the CA method. Chen et al [13,14] predicted microstructural evolution during DRX of an ultra-super-critical rotor steel. In addition, the DRX behavior of TRIP steel [15], C-Mn micro-alloy steel [16], Ti-alloy [17] and Ni-based alloy [18] was studied by using improved CA models.…”

Section: Introductionmentioning

confidence: 99%

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Mesoscale modeling and simulation of microstructure evolution during dynamic recrystallization of a Ni-based superalloy

Chen

Cui

et al. 2016

Appl. Phys. A

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Microstructural evolution and plastic flow characteristics of a Ni-based superalloy were investigated using a simulative model that couples the basic metallurgical principle of dynamic recrystallization (DRX) with the twodimensional (2D) cellular automaton (CA). Variation of dislocation density with local strain of deformation is considered for accurate determination of the microstructural evolution during DRX. The grain topography, the grain size and the recrystallized fraction can be well predicted by using the developed CA model, which enables to the establishment of the relationship between the flow stress, dislocation density, recrystallized fraction volume, recrystallized grain size and the thermomechanical parameters.

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Cellular Automaton‐Based Modeling of Recrystallization Texture Considering Instability Criterion and Nonuniform Boundary Mobility in AA1100 Aluminum Alloy

et al. 2023

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Herein, the nucleation instability criteria and nonuniform mobility are considered in the 2D cellular automaton simulation to investigate the recrystallization texture of cold‐rolled AA1100. Under the consideration of mechanical instability, the critical driving force for nucleation depends on the differences of the stored energy between a center cell and the neighboring cells. The possible nucleation sites are reduced to 37.5% in comparison with the nucleation model using a constant value of the driving force. The simulation of nucleation reveals that the mechanical instability promotes the formation of recrystallization textures at the expense of neighboring grains with orientations of deformation textures. The nonuniform mobility of high‐angle grain boundaries is implemented in simulation of recrystallization. The enhanced mobility of high‐angle grain boundaries with misorientation of 40°/<111> significantly promotes the growth of grains with orientations of Cube and RC20°RD (recrystallization textures) and inhibits the growth of grain with orientations of C, S, and B (deformation textures).

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Mesoscale simulation of microstructure evolution during multi-stage hot forging processes

Cited by 26 publications

References 49 publications

Prediction of microstructural evolution during hot forging

Prediction of microstructural evolution during hot forging

Mesoscale modeling and simulation of microstructure evolution during dynamic recrystallization of a Ni-based superalloy

Cellular Automaton‐Based Modeling of Recrystallization Texture Considering Instability Criterion and Nonuniform Boundary Mobility in AA1100 Aluminum Alloy

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