Modelling the dynamic recrystallization in C–Mn micro-alloyed steel during thermo-mechanical treatment using cellular automata

Timoshenkov, Alexander; Warczok, Piotr; Albu, Mihaela; Klarner, Jürgen; Kozeschnik, Ernst; Bureau, Romain; Sommitsch, Christof

doi:10.1016/j.commatsci.2014.02.017

Cited by 36 publications

(14 citation statements)

References 22 publications

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“…The average diameter of recrystallized grains at the end of the process is given in Table 3, which compares the simulated and experimental values. Again, the model reproduces behavior expected based on experimental investigations: the average grain size generated at the end of primary recrystallization is independent of the treatment temperature 27,29 . According to Table 3, the difference between the experimental and simulated average grain size is small and lies within the margin of error.…”

supporting

confidence: 80%

Simulation of CP-Ti Recrystallization and Grain Growth by a Cellular Automata Algorithm: Simulated Versus Experimental Results

2017

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The application of cellular automata in materials science requires the conversion of the automata's rules and abstract general properties to rules and properties associated with the material and phenomena under study. In this paper we propose a model which uses cellular automata to simulate recrystallization and grain growth during isothermal and non-isothermal treatments of cold worked polycrystalline materials. The algorithm's spatial and temporal scaling is based on known experimental results for recrystallization and grain growth in highly cold-worked commercially pure titanium grade 2. In the recrystallization, the best agreement between experimental and computational results in terms of the process kinetics and the average diameter of recrystallized grains is obtained from a nucleation model that considers the temperature-dependent nuclei formation rate. In the simulation of grain growth after primary recrystallization, the results indicate the normal growth of an equiaxed grain structure whose kinetics and dimensions are comparable to those observed experimentally.

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supporting

confidence: 80%

Simulation of CP-Ti Recrystallization and Grain Growth by a Cellular Automata Algorithm: Simulated Versus Experimental Results

2017

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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%

“…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. However, in order to perfect the classical CA model for DRX, the following two aspects still need further investigation.…”

Section: Introductionmentioning

confidence: 99%

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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“…It is well known that cellular automaton (CA) is a typical algorithm that is able to represent the discrete spatial and temporal evolution of complex system, where a local or global transformation rule is used for the involved cells [13,14]. In addition, the transformation rule, which is likely to be deterministic or probabilistic, is used to determine the state variables of a lattice point according to its previous state variables along with the state variables of the neighboring sites [15,16].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Dynamic Recrystallization of FeMnSiCrNi Shape Memory Alloy under Hot Compression Based on Cellular Automaton

Wang

Xing

Zhang

et al. 2019

Metals

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Dynamic recrystallization (DRX) takes place when FeMnSiCrNi shape memory alloy (SMA) is subjected to compression deformation at high temperatures. Cellular automaton (CA) simulation was used for revealing the DRX mechanism of FeMnSiCrNi SMA by predicting microstructures, grain size, flow stress, and dislocation density. The DRX of FeMnSiCrNi SMA has a characteristic of repeated nucleation and finite growth. The size of recrystallized grains increases with increasing deformation temperatures, but it decreases with increasing strain rates. The increase of deformation temperature leads to the decrease of the flow stress, whereas the increase in strain rate results in the increase of the flow stress. The dislocation density exhibits the same situation as the flow stress. The simulated results were supported by the experimental ones very well. Dislocation density is a crucial factor during DRX of FeMnSiCrNi SMA. It affects not only the nucleation but also the growth of the recrystallized grains. Occurrence of DRX depends on a critical dislocation density. The difference between the dislocation densities of the recrystallized and original grains becomes the driving force for the growth of the recrystallized grains, which lays a solid foundation for the recrystallized grains growing repeatedly.

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Modelling the dynamic recrystallization in C–Mn micro-alloyed steel during thermo-mechanical treatment using cellular automata

Cited by 36 publications

References 22 publications

Simulation of CP-Ti Recrystallization and Grain Growth by a Cellular Automata Algorithm: Simulated Versus Experimental Results

Simulation of CP-Ti Recrystallization and Grain Growth by a Cellular Automata Algorithm: Simulated Versus Experimental Results

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

Investigation of the Dynamic Recrystallization of FeMnSiCrNi Shape Memory Alloy under Hot Compression Based on Cellular Automaton

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