Cellular Automaton Modeling of Dynamic Recrystallisation Microstructure Evolution for 316LN Stainless Steel

Ji, Hai Peng; Zhang, Li Ge; Liu, Jing; Wang, Tai Yong

doi:10.4028/www.scientific.net/kem.693.548

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…They reported an adaptive response surface method as an optimization model to provide input parameters of the CA model. Recently, Li et al [18] and Haipeng et al [19] have investigated the effects of process parameters on the fraction of DRX and the average recrystallization grain size using the CA approach, and showed that the results are in good agreements with the experiments. Generally in CA modeling, initial grain size, initial grain orientation, and dislocation density are used as input data and flow curve, dislocation density, final grain size, and DRX volume fraction are the output data [20,21].…”

Section: Introductionmentioning

confidence: 83%

Dynamic recrystallization and texture modeling of IN718 superalloy

Azarbarmas

Aghaie-Khafri

2017

Modelling Simul. Mater. Sci. Eng.

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In the present study, dynamic recrystallization (DRX) of IN718 superalloy was modeled based on the experimental data as well as modified cellular automaton (CA) methods. To verify the ability of the presented models for predicting the microstructural features, the electron backscattered diffraction technique was used. Results showed that both models are capable of estimating the mean grain size and volume fraction of DRX grains. Furthermore, the macroscopic flow behavior of the material, and texture evolution of the structure were simulated using the proposed CA model. In this model, the lattice rotation of grains was determined assuming that each grain obeys the rules of single crystal deformation, and the morphological texture was obtained using a topological module in the CA approach.

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Section: Introductionmentioning

confidence: 83%

Dynamic recrystallization and texture modeling of IN718 superalloy

Azarbarmas

Aghaie-Khafri

2017

Modelling Simul. Mater. Sci. Eng.

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“…The boundary condition, also referred to as periodic condition, is the first condition type. It is used, among other things, for the simulation of the recrystallization process of hot deformed austenite of TRIP steel [ 23 ], in the modeling of grain coarsening and refinement during the dynamic recrystallization of pure copper [ 24 ], in two-dimensional CA with the quadrilateral element, or in the modeling of dynamic recrystallization microstructure evolution for 316LN stainless steel [ 25 ]. This type of boundary connects the boundaries of the cellular space and allows introduction of the dependence between their different parts.…”

Section: Boundary Conditionsmentioning

confidence: 99%

Modeling of Microstructure Evolution during Deformation Processes by Cellular Automata—Boundary Conditions and Space Reorganization Aspects

Łach

2021

Materials

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Cellular automata (CA) are efficient and effective numerical tools for modeling various phenomena and processes, e.g., microstructure evolution in plastic working processes. In many cases, the analysis of phenomena can be carried out only in a limited space and on representative volume. This limitation determines the geometry of CA space hence boundary conditions are very important issues in modeling. The paper discusses different boundary conditions that can be applied to modeling. Taking into account the transformation of the modeling space, the model should allow the selection of boundary conditions. The modeling of certain phenomena and processes is directly related to changes in the geometry of a representative volume and therefore may require changes or reorganization of the modeled CA space. Four reorganization options are presented: halving, cutting and bonding, doubling, and straightening. A choice of boundary conditions may depend on particular space reorganization as used for the modeling of microstructure evolution. A set of decision rules for selecting space reorganization options taking into account the changes of CA shape and sizes is also presented. The modeling of flat and shape rolling processes utilizing some of the described techniques is shown.

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“…They implemented the input parameters by using an adaptive response surface method. Recently, Haipeng et al [34], Zhang et al [35] and Li et al [36] have studied influences of the hot deformation variables on the microstructural features of recrystallized grains via the CA approach, and proved the high precision of the simulated results. Azarbarmas and Aghaie-Khafri [37] have lately presented a CA model coupled with a rate-dependent model to simulate the DDRX phenomenon during the hot deformation of Inconel 718, and showed the good ability of the CA model in coupling with other models.…”

Section: Introductionmentioning

confidence: 99%

A Combined Method to Model Dynamic Recrystallization Based on Cellular Automaton and a Phenomenological (CAP) Approach

Azarbarmas

Mirjavadi

Ghasemi

2018

Metals

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Titanium alloys with high stacking-fault energy show continuous dynamic recrystallization (CDRX) instead of discontinuous dynamic recrystallization (DDRX) during high-temperature deformation. During the CDRX mechanism, new recrystallized grains are generated by the progressive increasing of the low-angle boundary misorientations. In the present work, the CDRX phenomenon was modeled by using a cellular automaton (CA)-based method. The size of seeds was determined based on a phenomenological approach, and then the number and distribution of recrystallized grains as well as the topological changes were applied by utilizing the CA approach. In order to verify the capacity of the proposed model for predicting the microstructural characteristics, the experimental data of the hot-compressed TiNiFe alloy were used. Results showed that the presented model can accurately estimate the fraction of the recrystallized area. Moreover, the macroscopic flow curves of the alloy were well predicted by the present model.

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Cellular Automaton Modeling of Dynamic Recrystallisation Microstructure Evolution for 316LN Stainless Steel

Cited by 8 publications

References 7 publications

Dynamic recrystallization and texture modeling of IN718 superalloy

Dynamic recrystallization and texture modeling of IN718 superalloy

Modeling of Microstructure Evolution during Deformation Processes by Cellular Automata—Boundary Conditions and Space Reorganization Aspects

A Combined Method to Model Dynamic Recrystallization Based on Cellular Automaton and a Phenomenological (CAP) Approach

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