Large-deformation crystal plasticity simulation of microstructure and microtexture evolution through adaptive remeshing

Sedighiani, Karo; Shah, Vitesh; Τράκα, Κωνσταντίνα; Diehl, Martin; Roters, Franz; Sietsma, Jilt; Raabe, Dierk

doi:10.1016/j.ijplas.2021.103078

Cited by 30 publications

(14 citation statements)

References 64 publications

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“…In the present paper the role of microstructural topology was considered in mean-field crystallographically resolved models. In the proposed analysis the topological features of the microstructure were described according to a method applied in the ALAMEL model by Van Houtte et al [5], whereby a texture is represented by an even number of N orientations that are grouped in pairs. By ordering these grains in a linear sequence and selecting the pair groups on the basis of the set average pair misorientation, topological aspects can be added to the model.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Traka et al [3] reported that a CPFE model in combination with a cellular automaton model can be employed to simulate thermally activated processes such as recovery and static recrystallization. Numerically CPFE models benefit from the Fast Fourier spectral solver method, which allows to speed up drastically numerical integration compared to conventional solvers [4,5]. Such models are capable of simulation microstructural evolutions in a full-field mode taking into account the full spectrum of (sub-)structural heterogeneities.…”

Section: Introductionmentioning

confidence: 99%

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Topological aspects of mean-field crystallographically resolved models

Kestens

Minh

Avendaño

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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It is well-known that the crystallographic texture of a polycrystalline aggregate can be represented by the Orientation Distribution Function (ODF). A similar statistical approach can be extended to other microstructural state variables that are of relevance in the context of obtaining microstructurally based and quantitatively accurate structure-properties relations. In principle such statistical representations are of a non-topological nature, in contrast to an RVE (Representative Volume Element) description of the microstructure. However, by including additional variables to the statistical descriptor specific features of the topology may be taken into account. In this paper the example will be shown on how the plastic anisotropy simulation of a conventional deep drawing grade of Interstitial Free (IF) steel can be improved by considering the crystallographic misorientation of pairs of neighboring crystals, which represent the basic structural units of the 2-point mean field ALAMEL crystal plasticity model. In another example it will be shown how the recrystallization texture of the same deep drawing IF steel can be modelled with improved accuracy if the Strain Induced Boundary Mechanism (SIBM) is taken into account whereby a crystal orientation of low stored energy grows into a neighboring orientation of high stored energy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Topological aspects of mean-field crystallographically resolved models

Kestens

Minh

Avendaño

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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“…The regridding methodology used in this study is based on the approach introduced by Sedighiani et al [33]. In this regridding technique, a new undistorted grid is created that matches the average deformed grid in the original analysis.…”

Section: Deformed Geometry and Regriddingmentioning

confidence: 99%

“…However, incorporating deformed grids in cellular automata is complicated, because it leads to large computational overheads for the calculation of the transformation rules in three dimensions. One way to overcome these difficulties is to transfer the data from the deformed grid (along with fluctuations of deformation) to a regular grid for the cellular automaton using a regridding 1 approach as described by Sedighiani et al [33]. Recently, an algorithm for implementing curvature driven grain boundary migration into a cellular automaton model has been presented [34].…”

Section: Introductionmentioning

confidence: 99%

“…Here, a two-way coupling of a large-strain crystal plasticity framework (DAMASK) [35] with cellular automaton model (CASIPT) [36] is presented. The mapping between the deformed mesh of the crystal plasticity model and the regular grid of the cellular automaton is realized with the help of a recently developed regridding scheme [33]. Section 2 gives relevant details regarding DAMASK and CASIPT, and explains how these models are coupled together.…”

Section: Introductionmentioning

confidence: 99%

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Coupling Crystal Plasticity and Cellular Automaton Models to Study Meta-Dynamic Recrystallization During Hot Rolling at High Strain Rates

Shah¹,

Sedighiani²,

Dokkum³

et al. 2022

SSRN Journal

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Influence of Strain Rate Sensitivity on Cube Texture Evolution in Aluminium Alloys

Cantergiani

Falkinger²,

Mitsche

et al. 2022

Metall Mater Trans A

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The influence of strain rate sensitivity on development of Cube texture and on the morphology of Cube-oriented grains is often neglected in simulations approaches. Therefore, crystal plasticity simulations and experiments were performed up to 73 pct of thickness reduction for cold rolling on Al 6016. It is found, that low values of strain rate sensitivity promote Cube grains fragmentation and avoid formation of transition bands already at 50 to 55 pct thickness reduction. High values of strain-rate sensitivity cause formation of Cube transition bands leaving thin Cube grains in the microstructure and delay their fragmentation. Other texture components are affected by changes in strain rate sensitivity as well. The Copper volume fraction in the final texture diminishes as the strain rate sensitivity decreases, while Brass and S components of the beta fiber show a moderately higher volume fraction when the strain rate sensitivity increases. The final volume fraction of Goss is highest when the strain rate sensitivity is 10−2 but low if the strain rate sensitivity is 10−3 or raises up to 10−1. Recrystallization texture components (P, Q) are not affected by strain rate sensitivity, while the invGoss fraction decreases for high values of strain rate sensitivity. The results found in cold rolling crystal plasticity simulations were compared with experimentally determined Cube distribution and texture components obtained through thickness for Al6016 rolled at 80 m/min and 600 m/min. Further crystal plasticity simulations were performed to predict the influence of strain rate sensitivity during several hot rolling conditions where activity of non-octahedral slip systems was included in the simulations. During hot rolling, high values of strain rate sensitivity contribute to Cube stabilization and promote formation of Copper texture and delay Brass and S.

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Large-deformation crystal plasticity simulation of microstructure and microtexture evolution through adaptive remeshing

Cited by 30 publications

References 64 publications

Topological aspects of mean-field crystallographically resolved models

Topological aspects of mean-field crystallographically resolved models

Coupling Crystal Plasticity and Cellular Automaton Models to Study Meta-Dynamic Recrystallization During Hot Rolling at High Strain Rates

Influence of Strain Rate Sensitivity on Cube Texture Evolution in Aluminium Alloys

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