A comparative study of the effect of random and preferred crystallographic orientations on dynamic recrystallization behavior using a cellular automata model

Alone, Ashish; Chatterjee, Ritam; Alankar, Alankar

doi:10.1016/j.mtcomm.2020.101200

Cited by 8 publications

(10 citation statements)

References 46 publications

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“…There is a significant difference between the final DRX fraction at different temperatures. This finding is in agreement with several references, such as [54,55]. As shown in figure 13(b), a higher fraction of grains is recrystallized at lower strain rate, although the difference is not considerable across strain rates.…”

Section: Grain Size Evolutionsupporting

confidence: 92%

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Evaluating the influence of deformation variables on dynamic recrystallization behavior using a crystal plasticity model

Chatterjee

Murty

Alankar

2023

Modelling Simul. Mater. Sci. Eng.

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The present study is an attempt to model dynamic recrystallization (DRX) in a single phase metal using a mean field crystal plasticity (MFCP) based approach. A new empirical equation is proposed to model nucleation, in which the nucleation rate is a function ofmicrostructure and plasticity descriptors that are known to affect DRX behavior, such as the temperature, strain rate, grain fineness and stored energy. Grains undergo nucleation when their dislocation density exceeds a threshold value. Subsequently, new grains grow based on the difference in stored deformation energy with respect to the average value over all grains. The MFCP-DRX model is able to correctly predict trends for the flow stress, dislocation density evolution, grain size evolution and kinetics across a range of temperatures and strainrates for uniaxial compression. Transition of the flow stress from single to multiple peaks is observed with increasing temperature and decreasing strain rate, thus comparing well against known DRX trends. The evolutions of crystallographic texture during DRX in unaxialcompression and plane strain compression are compared against experimental observations. A sensitivity analysis is conducted to understand the effect of variables on nucleation and growth. The competition between nucleation and growth dominated deformation in different strain regimes is analyzed in detail across various temperatures and strain rates.

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Section: Grain Size Evolutionsupporting

confidence: 92%

“…As shown in figure 13(b), a higher fraction of grains is recrystallized at lower strain rate, although the difference is not considerable across strain rates. This trend has earlier been observed in several modeling efforts [20,55].…”

Section: Grain Size Evolutionsupporting

confidence: 82%

Evaluating the influence of deformation variables on dynamic recrystallization behavior using a crystal plasticity model

Chatterjee

Murty

Alankar

2023

Modelling Simul. Mater. Sci. Eng.

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“…In the studies of CA simulation of DRX microstructure during thermal deformation, the vast majority of dislocation mechanisms are used to model dynamic recrystallization of metal-forming processes [ 65 , 66 , 67 , 68 ]. During thermal deformation, the dislocation density is increased with the increase in strain; the stored energy after deformation becomes the nucleation driving force for recrystallization.…”

Section: Application Progress Of Ca In Microstructure Simulationmentioning

confidence: 99%

Review on Cellular Automata for Microstructure Simulation of Metallic Materials

Zhi,

Jiang,

et al. 2024

Materials

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The cellular automata (CA) method has played an important role in the research and development of metallic materials. CA can interpret the microstructure changes of materials and obtain more abundant, accurate and intuitive information of microstructure evolution than conventional methods. CA can visually represent the process of grain formation, growth, development and change to us in a graphical way, which can assist us in analysis, thinking and solving problems. In the last five years, the application of CA in materials research has been rapidly developed, and CA has begun to occupy an increasingly important position in the simulation research of metallic materials. After introducing the advantages and limitations of CA compared to other widely used simulation methods, the purpose of this paper is to review the recent application progress on the microstructure simulation of metallic materials using CA, such as solidification, recrystallization, phase transformation and carbide precipitation occurring during forming and heat treatment. Specifically, recent research advances on microstructure simulation by CA in the fields of additive manufacturing, welding, asymmetrical rolling, corrosion prevention, etc., are also elaborated in this paper. Furthermore, this paper points out the future work direction of CA simulation in the research of metallic materials, especially in the simulation of the crystal structure, the prediction of mechanical properties, CA simulation software and rule systems, etc. These are expected to attract wide attention of researchers in the field of metallic materials and promote the development of CA in materials research.

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“…Many researchers use the method to simulate grain growth, texture evolution, and recrystallization phenomena. [43][44][45][46] Conceptually, different are the discrete modeling methods, Monte Carlo (MC) [47,48] and cellular automata (CA) [49,50] techniques, which can also easily incorporate stochastic aspects of investigated phenomena into the numerical solution.…”

Section: Introductionmentioning

confidence: 99%

“…Conceptually, different are the discrete modeling methods, Monte Carlo (MC) [ 47,48 ] and cellular automata (CA) [ 49,50 ] techniques, which can also easily incorporate stochastic aspects of investigated phenomena into the numerical solution.…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Cellular Automata‐Based Full‐Field Models of Static Recrystallization: A Perspective Review

Madej

Sitko

2022

steel research int.

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The evolution of metallic material microstructures under thermomechanical treatment is a critical element that influences the in‐use properties of the final product. In the last four decades, many researchers around the world focused on reliable numerical recreation of phenomena that occur during metal processing. Particular attention is put on the phase transformation, texture evolution, and recrystallization predictions to simulate material behavior at the subsequent processing stages. In recent years, approaches taking into account explicit representation of morphological elements during simulation are becoming more frequently used even at the industrial scale. One of those methods addressed within the current article in application to static recrystallization (SRX) modeling is the cellular automata (CA) approach. A review of the CA‐based full‐field SRX models highlights the progress in capabilities of developed approaches in consideration of various mechanisms controlling microstructure evolution like heterogeneous energy distribution, probabilistic or physics‐based nucleation, grain growth driven by different driving forces, or grain boundary migration is presented within the article. The issue of the computational time associated with 3D CA modeling is particularly addressed. Possible approaches to reducing simulation times based on efficient use of modern computer architecture are finally evaluated as guidelines for further model development.

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A comparative study of the effect of random and preferred crystallographic orientations on dynamic recrystallization behavior using a cellular automata model

Cited by 8 publications

References 46 publications

Evaluating the influence of deformation variables on dynamic recrystallization behavior using a crystal plasticity model

Evaluating the influence of deformation variables on dynamic recrystallization behavior using a crystal plasticity model

Review on Cellular Automata for Microstructure Simulation of Metallic Materials

Computationally Efficient Cellular Automata‐Based Full‐Field Models of Static Recrystallization: A Perspective Review

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