Neighborhood effect within polycrystalline materials: Elastoplastic micromechanical analysis by cellular automaton and finite element

Bretin, R.; Lévesque, Martin; Pilvin, Philippe; Bocher, Philippe

doi:10.1016/j.ijfatigue.2020.105634

Cited by 3 publications

(2 citation statements)

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“…It can be in 1D, 2D, and 3D spaces. [66][67][68] 3) Transition rules f-functions that control the change in the state of each CA cell in the CA space with respect to the previous states of its neighbors and the cell itself…”

Section: Ca Models Of Static Recrystallizationmentioning

confidence: 99%

“…It can be in 1D, 2D, and 3D spaces. [ 66–68 ] 3) Transition rules f —functions that control the change in the state of each CA cell in the CA space with respect to the previous states of its neighbors and the cell itself

Y_{i}^{t + 1} = f (Y_{j}^{normalt}), j \in N (i)

where N(i) is the i th cell neighborhood; Y i is the i th cell state.…”

Section: Ca Models Of Static Recrystallizationmentioning

confidence: 99%

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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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Section: Ca Models Of Static Recrystallizationmentioning

confidence: 99%