Multiscale modeling of discontinuous dynamic recrystallization during hot working by coupling multilevel cellular automaton and finite element method

Chen, Fei; Zhu, Huajia; Chen, Wen; Ou, Hengan; Cui, Zhenshan

doi:10.1016/j.ijplas.2021.103064

Cited by 55 publications

(19 citation statements)

References 86 publications

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“…Recrystallization includes the formation of new grains in the deformed material and the subsequent migration of high angle boundaries, the main driving force of which is the energy stored on the defects formed during inelastic deformation [ 15 ]. There are three main types of dynamic recrystallization: continuous [ 16 , 17 ], discontinuous [ 18 , 19 ] and geometric recrystallizations [ 20 , 21 ]. An important material parameter that determines the sequence of changes in the defect and subgrain structure is the stacking fault energy (SFE) ([ 5 , 12 , 13 ] etc.).…”

Section: Introductionmentioning

confidence: 99%

“…This is followed by the homogeneous formation of nuclei, namely, their evolutionary transition to new grains, known as continuous recrystallization [ 16 , 17 ]. At low SFE values, the formation of recrystallization nuclei occurs in a heterogeneous way in the crystal lattice sites with significant distortions, and is called discontinuous or classical recrystallization [ 5 , 18 , 19 ]. Another type of recrystallization is geometric recrystallization which is associated with the material processing.…”

Section: Introductionmentioning

confidence: 99%

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Subgrain Coalescence Simulation by Means of an Advanced Statistical Model of Inelastic Deformation

et al. 2022

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The development of technological methods for processing and manufacturing of functional (with a priori targeted properties) polycrystalline materials and products made of these materials still remains an acute problem. A multilevel modeling approach offers researchers the opportunity to describe inelastic deformation by applying internal variables that give an effective characterization of the material structure at different structural scale levels. High temperature plastic deformation is accompanied by these processes, which leads to a significant rearrangement of the meso- and microstructure of the material. The most substantial contribution to changing the properties of polycrystals is made by the evolution of grain and defect structures at the expense of dynamic recrystallization, which significantly depends on dynamic recovery. In this paper, we consider the problem of the coalescence of subgrains undergoing rotation during inelastic hot deformation. This process is called subgrain coalescence, and it is one of the dynamic recovery mechanisms responsible for changes in the fine subgrain structure. Under applied thermomechanical loads, the coalescence process promotes the formation of recrystallization nuclei and their subsequent growth, which can greatly change the grain structure of a polycrystal. The problem was solved in terms of the advanced statistical model of inelastic deformation, modified to describe the subgrain coalescence process. The model takes into account the local interactions between contacting structural elements (subgrains). These have to be considered so that the grain coalescence caused by a decrease in subboundary energies during their progressive merging can be adequately analyzed. For this purpose, a subgrain structure quite similar to the real structure was modeled using Laguerre polyhedra. Subgrain rotations were investigated using the developed model, which relies on the consideration of the excess density edge component of the same sign dislocations on incidental subgrain boundaries. The results of modeling of a copper polycrystal are presented, and the effects of temperature and strain rate on the subgrain coalescence process is demonstrated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subgrain Coalescence Simulation by Means of an Advanced Statistical Model of Inelastic Deformation

et al. 2022

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“…[31] In alloys, alloying elements may hinder grain growth. Chen et al [32] characterized this effect by introducing a solute drag effect coefficient λ. Liu et al [12] introduced the solute resistance parameter β to consider this effect. Therefore, the modified equation for the distance of grain boundary migration for alloys can be expressed as…”

Section: Grain Growth Modelmentioning

confidence: 99%

Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel

Ren

Huo

et al. 2023

steel research int.

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Herein, isothermal compression experiments are conducted on EA4T steel at 970–1170 °C, with strain rates of 0.01–1.0 s−1 and a strain of 0.2–0.8 s−1. Based on the experimental data, a high‐temperature constitutive model is developed for EA4T steel. The activation energy of dynamic recrystallization (DRX) is calculated to be 383 666 J mol−1, and the correlation coefficient and root mean square error between the results of the constitutive model and experimental results are 0.9943 and 4.6823, respectively. The average grain size for each deformation condition is determined using the linear‐intercept method. The grain growth model widely used in cellular automaton (CA) simulations is found unsuitable for EA4T steel. Therefore, a modified CA model of DRX behavior suitable for EA4T steel is developed. The nucleation rates and solute drag effect coefficients under different deformation conditions are determined. Furthermore, simulations are performed under other deformation conditions using the CA model. The simulated results for the average grain size, microstructure morphology, and DRX fraction agree well with the experimental results. The reason for the deviation between the observed and simulated DRX fractions is also explored.

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“…These models may lack the ability to describe the characteristics of the grain structure, such as its morphology and grain distribution. Cellular automaton (CA) has an intrinsic advantage over the description and visualization of microstructural evolution in metals and alloys [19,20]. It has been successfully applied to describe the evolution of the grain structure in alloy steels [21,22], titanium alloys [23][24][25], and Ni-based superalloys [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Study of Grain Growth in a Ni-Based Superalloy by Experiments and Cellular Automaton Model

Liu

et al. 2021

Materials

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The grain growth behavior in a typical Ni-based superalloy was investigated using isothermal heat treatment experiments over a holding temperature range of 1353–1473 K. The experimental results showed that the grain structure continuously coarsened as the holding time and holding temperature increased during heat treatment. A classical parabolic grain growth model was used to explore the mechanism of grain growth under experimental conditions. The grain growth exponent was found to be slightly above 2. This indicates that the current grain growth in the studied superalloy is mainly governed by grain boundary migration with a minor pinning effect from the precipitates. Then, the grain growth in the studied superalloy during isothermal heat treatment was modelled by a cellular automaton (CA) with deterministic state switch rules. The microscale kinetics of grain growth is described by the correlation between the moving velocity and curvature of the grain boundary. The local grain boundary curvature is well evaluated by a template disk method. The grain boundary mobility was found to increase with increasing temperature. The relationship between the grain boundary mobility and temperature has been established. The developed CA model is capable of capturing the dependence of the grain size on the holding time under different holding temperatures.

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Multiscale modeling of discontinuous dynamic recrystallization during hot working by coupling multilevel cellular automaton and finite element method

Cited by 55 publications

References 86 publications

Subgrain Coalescence Simulation by Means of an Advanced Statistical Model of Inelastic Deformation

Subgrain Coalescence Simulation by Means of an Advanced Statistical Model of Inelastic Deformation

Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel

Study of Grain Growth in a Ni-Based Superalloy by Experiments and Cellular Automaton Model

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