Modeling the kinetics and microstructural evolution during static recrystallization—Monte Carlo simulation of recrystallization

Radhakrishnan, Balasubramaniam; Sarma, G.B.; Zacharia, T.

doi:10.1016/s1359-6454(98)00077-9

Cited by 146 publications

(80 citation statements)

References 26 publications

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“…The mapping is carried out by using a procedure described elsewhere [6]. The .where 8 is the misorientation between two subgrains, T~ is energy per unit area of a high-angle boundary, and 6* is the misorientation limit for low angle boundaries, which is usually taken as 15".…”

Section: Computational Approachmentioning

confidence: 99%

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Coupled finite element-Monte Carlo simulation of microstructure and texture evolution during thermomechanical processing

Radhakrishnan¹,

Sarma²,

Zacharia³

1998

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A novel simulation technique for predicting the microstructure and texture evolution during thermomechanical processing is presented. The technique involves coupling a finite element microstructural deformation model based on crystal plasticity with a Monte Carlo simulation of recovery and recrystallization. The finite element model captures the stored energy and the crystallographic orientation distributions in the deformed microstructure. The Monte Carlo simulation captures the microstructural evolution associated with recovery and recrystallization. A unique feature of the Monte Carlo simulation is that it treats recrystallization as a heterogeneous subgrain growth process, thus providing the natural link between nucleation and growth phenomena, and quantifying the role of recovery in these phenomena. Different nucleation mechanisms based on heterogeneous subgrain growth as well as strain induced boundary migration are automatically included in the recrystallization simulation. The simulations are shown to account for the extent of prior deformation on the microstructure and kinetics of recrystallization during subsequent annealing. The simulations also capture the influence of the presence of cube orientations in the initial microstructure, and the operation of non-octahedral slip during deformation of fcc polycrystals, on the recrystallization texture.

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Section: Computational Approachmentioning

confidence: 99%

“…Recently, the MC simulation of recrystallization was coupled to a finite element (FE) simulation of microstructural deformation based on crystal plasticity [5,6]. The crystal plasticity model provided a quantitative description of the stored energy and orientation distributions in the deformed microstructure.…”

Section: Introductionmentioning

confidence: 99%

Coupled finite element-Monte Carlo simulation of microstructure and texture evolution during thermomechanical processing

Radhakrishnan¹,

Sarma²,

Zacharia³

1998

Self Cite

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“…Mesoscopic computer simulation provides an alternative approach to experimentation. Over the last decades, many computational simulations of recrystallization have been reported, such as Monte Carlo (MC) model [3], cellular automaton (CA) models [5][6] and phase field models [7][8]. In these computational approaches, The Monte Carlo method and cellular automaton (CA)'s unrealistic assumption lead to the results of the simulation deviates from the experimental observations.…”

Section: Introductionmentioning

confidence: 99%

Phase Field Simulation of Static Recrystallization Considering Deformation Microstructures and Inhomogeneous Distribution of Stored Energy Based on Lattice Deformation Model

Li¹,

Liu²,

Xiong³

et al. 2017

dtmse

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Abstract.A numerical model of recrystallization taking the deformation and the inhomogeneous distribution of stored energy into account were proposed in this paper. Here the deformed microstructure is obtained by a lattice deformation model and the microstructure evolution during recrystallization is simulated by a multi-state free energy phase field method. According to the characteristics of the different deformation zone and the inhomogeneous distribution of the stored energy, the weight factor λα reflected the distribution of the stored energy in the deformed zones is introduced in this work. As for the density of dislocation, the stored energy in the grain boundary and the triple junctions are double and 2.5 times higher than that within the grain, respectively. We utilize the multi-state free energy function to simulate the recrystallization process of the 0.11C-0.21Si-1.40Mn-0.04Nb steel. The results of simulation match very well with the experiments in the 0.11C-0.21Si-1.40Mn-0.04Nb steel. Therefore, through these models, it provides an excellent resolution to the problem that the deformation and nucleation affection on the process of the recrystallization. It is confirmed that recrystallization simulation depending on the lattice deformation model and taking the inhomogeneous distribution of the store energy into account can be successfully proposed to simulate practical situation.

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“…However, the ideal JMAK behavior is rarely exhibited by real materials. Such deviation from this ideal behavior is due to the presence of recovery, non-uniform distribution of stored strain energy, non-random distribution of recrystallized nuclei, and anisotropic growth of the recrystallized nuclei [22]. For this category of models, experimental validation is often lacking, and good agreement between numerical and experimental data in terms of grain size is rare.…”

Section: Introductionmentioning

confidence: 99%

A mean field model of dynamic and post-dynamic recrystallization predicting kinetics, grain size and flow stress

Beltran

Huang

Logé

2015

Computational Materials Science

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a b s t r a c tA physically-based two-site mean field model has been developed to describe the microstructural evolution due to recrystallization during and after deformation. The model has been applied to predict the recrystallized fraction, recrystallized grain size, and flow stress of 304L austenitic stainless steel during discontinuous dynamic recrystallization (DDRX), post-dynamic recrystallization (PDRX) and grain growth (GG). The model parameters vary with temperature and strain rate but do not depend on grain size. In PDRX and GG regime, the parameters only depend on temperature. The model responds well to conditions with different temperatures, strain rates, strains and/or annealing times. Particular attention is paid to the occurrence of two-stage growth in the recrystallized grain size plots when PDRX occurs. There is a good quantitative agreement between model predictions and experimental results obtained in the different recrystallization regimes, opening the possibility of modeling multi-pass operations compatible with industrial applications.

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Modeling the kinetics and microstructural evolution during static recrystallization—Monte Carlo simulation of recrystallization

Cited by 146 publications

References 26 publications

Coupled finite element-Monte Carlo simulation of microstructure and texture evolution during thermomechanical processing

Coupled finite element-Monte Carlo simulation of microstructure and texture evolution during thermomechanical processing

Phase Field Simulation of Static Recrystallization Considering Deformation Microstructures and Inhomogeneous Distribution of Stored Energy Based on Lattice Deformation Model

A mean field model of dynamic and post-dynamic recrystallization predicting kinetics, grain size and flow stress

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