Microstructure prediction of the austenite recrystallization during multi-pass steel strip hot rolling: A cellular automaton modeling

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

doi:10.1016/j.commatsci.2008.04.010

Cited by 111 publications

(58 citation statements)

References 33 publications

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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: 86%

“…Up to now, there are many cases of successful application of CA in modeling microstructural evolution. In these studies, Ding et al [5], Raabe [6], Kugler and Turk [7], Xiao and Zheng [8] and Jin and Cui [9] contributed greatly to the development of the CA method for the simulation of DRX from the aspects of optimization algorithm, grain morphology change and parameter identification. Recently, based on the work of Ding et al [5] and Kugler and Turk [7], Yazdipour et al [10] simulated DRX during thermomechanical deformation of 304 austenitic stainless steel by the CA method.…”

Section: Introductionmentioning

confidence: 99%

“…There are two basic methods to calculate the constant (C dynamic ). First, the percentage of DRX (g) can be experimentally measured for a specific deformation condition [8]; then, the constant C dynamic in Eq. (4) can be determined for a deforming material.…”

Section: Modeling Of Nucleation and Grain Growthmentioning

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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Section: Simulation Results and Discussionmentioning

confidence: 86%

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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“…Purely curvature-driven grain boundary migration is considered for example in [46]. Impurity effects on grain boundary migration is discussed in the review paper [47] and also in [6,13].…”

Section: Classical Results and Empirical Modelsmentioning

confidence: 99%

Approaches to Modeling of Recrystallization

Hallberg

2011

Metals

157

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Control of the material microstructure in terms of the grain size is a key component in tailoring material properties of metals and alloys and in creating functionally graded materials. To exert this control, reliable and efficient modeling and simulation of the recrystallization process whereby the grain size evolves is vital. The present contribution is a review paper, summarizing the current status of various approaches to modeling grain refinement due to recrystallization. The underlying mechanisms of recrystallization are briefly recollected and different simulation methods are discussed. Analytical and empirical models, continuum mechanical models and discrete methods as well as phase field, vertex and level set models of recrystallization will be considered. Such numerical methods have been reviewed previously, but with the present focus on recrystallization modeling and with a rapidly increasing amount of related publications, an updated review is called for. Advantages and disadvantages of the different methods are discussed in terms of applicability, underlying assumptions, physical relevance, implementation issues and computational efficiency.

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“…Recently, various numerical simulation techniques such as Potts-Monte Carlo models (MC) [11][12][13], phase field models [14,15] and cellular automaton models (CA) [16][17][18] have been gradually used to model the microstructure evolution on the mesoscale [19]. The CA methods have been widely used in these investigations, and have some advantages [20].…”

mentioning

confidence: 99%

Static coarsening of titanium alloys in single field by cellular automaton model considering solute drag and anisotropic mobility of grain boundaries

Yang

et al. 2012

Chin. Sci. Bull.

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Static coarsening is an important physical phenomenon that influences microstructural evolution and mechanical properties. How to simulate this process effectively has become an important topic which needs to be dealt with. In this paper, a new cellular automaton (CA) model, which considers the effect of solute drag and anisotropic mobility of grain boundaries, was developed to simulate static grain coarsening of titanium alloys in the beta-phase field. To describe the effect of the drag caused by different solute atoms on coarsening, their diffusion velocities in beta titanium were estimated relative to that of titanium atoms (Ti). A formula was proposed to quantitatively describe the relationship of the diffusion velocity of Ti to that of solute atoms; factors influencing the diffusion velocity such as solute atom radius, mass, and lattice type were considered. The anisotropic mobility of grain boundaries was represented by the parameter c 0 , which was set to 1 for a fully anisotropic effect. These equations were then implemented into the CA scheme to model the static coarsening of titanium alloys Ti-6Al-4V, Ti17 (Ti-5Al-4Mo-4Cr-2Sn-2Zr, wt%), TG6 (Ti-5.8Al-4.0Sn-4.0Zr-0.7Nb-1.5Ta-0.4Si-0.06C, wt%) and TA15 (Ti-6Al-2Zr-1Mo-1V, wt%) in the beta field. The predicted results, including coarsening kinetics and microstructural evolution, were in good agreement with experimental results. Finally, the effects of time, temperature, and chemical composition on grain coarsening and the limitations of the model were discussed. The mechanical properties of titanium alloys such as the creep resistance, fracture toughness and crack propagation resistance are determined by their grain sizes in the beta phase field [1,2]. However, the grains are prone to static coarsening when held in the beta phase field because of the structural characteristics of beta-titanium and relatively high stacking fault energy at high temperatures [3]; this results in a marked decrease in the mechanical properties of final products. Therefore, it is necessary to understand and to control static coarsening of titanium alloys in a single phase field to obtain the expected mechanical properties. Previously, many experimental investigations have focused on the static coarsening behaviors of titanium alloys in the beta phase field. Ivasishin et al. [4,5] investigated the effect of texture evolution on the values of the static coarsening exponent n and activation energy for Ti-6Al-4V alloy in the beta field. They found values of n ranging from 0.22 to 0.31 at different temperatures, but there were marked deviations attributed to different materials and experiments when the results were compared with those of others. Recently, Semiatin et al. [6][7][8] investigated the static grain coarsening of Ti-6Al-4V alloy via a series of heat treatments at typical temperatures. They found that the coarsening process of primary alpha particles was controlled by the volume diffusion of solute elements at lower temperatures and the growth exponent was equal to 0.33.

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Microstructure prediction of the austenite recrystallization during multi-pass steel strip hot rolling: A cellular automaton modeling

Cited by 111 publications

References 33 publications

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

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

Approaches to Modeling of Recrystallization

Static coarsening of titanium alloys in single field by cellular automaton model considering solute drag and anisotropic mobility of grain boundaries

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