Three-dimensional phase field modelling of the austenite-to-ferrite transformation

Militzer, Matthias; Mecozzi, M.G.; Sietsma, Jilt; Zwaag, Sybrand van der

doi:10.1016/j.actamat.2006.04.029

Cited by 123 publications

(83 citation statements)

References 30 publications

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“…However, the resulting geometrical differences between the present model and phase-field simulation are small as ferrite grains also grow more or less spherically in the phase-field simulations when f a < 0.3. [42] Although a considerable difference is observed in the transformation kinetics for these two models, the average grain size d s and the standard deviation r p of the grain size distribution, show a comparable evolution as a function of f a . The value of d s increases nearly at the same speed for both models in the intermediate transformation stage (0.2 < f a < 0.6).…”

Section: A Comparison Between the Present Model And The Phase-field mentioning

confidence: 98%

“…The present model is employed to simulate the ferrite transformation in an Fe-0.10C-0.49Mn (wt pct) steel during continuous cooling. The steel composition and transformation conditions were chosen equal to those in a previous computational study using a 3D phase-field model [20,42] which used the MICRESS (MICrostructure Evolution Simulation Software) code developed by Steinbach and coworkers. [43,44] The A 3 and A 1 À temperature of this steel are calculated to be 1116 K and 984 K (843°C and 711°C), respectively.…”

Section: Computational Proceduresmentioning

confidence: 99%

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Analysis of the Grain Size Evolution for Ferrite Formation in Fe-C-Mn Steels Using a 3D Model Under a Mixed-Mode Interface Condition

Fang

Mecozzi

Brück

et al. 2017

Metall Mater Trans A

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A 3D model has been developed to predict the average ferrite grain size and grain size distribution for an austenite-to-ferrite phase transformation during continuous cooling of an Fe-C-Mn steel. Using a Voronoi construction to represent the austenite grains, the ferrite is assumed to nucleate at the grain corners and to grow as spheres. Classical nucleation theory is used to estimate the density of ferrite nuclei. By assuming a negligible partition of manganese, the moving ferrite-austenite interface is treated with a mixed-mode model in which the soft impingement of the carbon diffusion fields is considered. The ferrite volume fraction, the average ferrite grain size, and the ferrite grain size distribution are derived as a function of temperature. The results of the present model are compared with those of a published phase-field model simulating the ferritic microstructure evolution during linear cooling of an Fe-0.10C-0.49Mn (wt pct) steel. It turns out that the present model can adequately reproduce the phase-field modeling results as well as the experimental dilatometry data. The model presented here provides a versatile tool to analyze the evolution of the ferrite grain size distribution at low computational costs.

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Section: A Comparison Between the Present Model And The Phase-field mentioning

confidence: 98%

Section: Computational Proceduresmentioning

confidence: 99%

Analysis of the Grain Size Evolution for Ferrite Formation in Fe-C-Mn Steels Using a 3D Model Under a Mixed-Mode Interface Condition

Fang

Mecozzi

Brück

et al. 2017

Metall Mater Trans A

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“…The M0 was found to depend on the cooling rate in order to describe experimental CCT data. Mecozzi et al 9) and Militzer et al 10) have used phase field modelling to simulate the austenite-to-ferrite transformation in Fe-0.1 mass% C-0.49 mass% Mn. Also, their analysis revealed an apparent cooling rate dependence of M0.…”

Section: )mentioning

confidence: 99%

Modelling Phase Transformation Kinetics in Fe–Mn Alloys

Jia

Militzer

2012

ISIJ Int.

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A unified model is proposed for the austenite-to-ferrite transformation kinetics in binary Fe-Mn alloys that accounts for solute drag of Mn. To aid the model development, continuous cooling transformation (CCT) tests were conducted for an interstitial-free steel that can be considered as Fe-0.1%Mn alloy. The experimental transformation data are supplemented with literature data for Fe-1%Mn and Fe-2%Mn alloys to establish a CCT database for Fe-Mn alloys. The austenite-to-ferrite transformation kinetics is described from a fundamental perspective by assuming an interface-controlled reaction and including solute drag of Mn. Using the solute drag model of Fazeli and Militzer, intrinsic interface mobility, trans-interface diffusivity of Mn and its binding energy have been determined from the CCT data. The interfacial parameters are critically analyzed and compared with independent measurements of diffusion and grain boundary segregation.

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“…(4) describes the phenomenological thermodynamic driving force of the transformation. The magnitude of the driving force ΔG i j is given by ΔG αγ = ΔS ΔT at the α/γ interface, where ΔS and ΔT are the entropy difference between the α and γ phases and the undercooling, respectively (28) . The mobility of the phase field M…”

Section: Multiphase Field Modelmentioning

confidence: 99%

Multiphase Field Simulation of Austenite-to-Ferrite Transformation Accelerated by GPU Computing

Yamanaka

Takaki

Aoki

et al. 2012

JCST

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The multiphase field (MPF) method is recognized as a powerful numerical method to simulate microstructural evolutions, such as solidification, grain growth, recrystallization, and phase transformation, in various materials. However, because we need to solve the time evolution equations for multiple-order parameters derived from the total Gibbs free energy, MPF simulations are very computationally expensive. In this paper, we use a graphics processing unit (GPU) to accelerate the two-dimensional MPF simulation of austenite-to-ferrite transformation in a Fe-C alloy. This is an important phenomenon for predicting the morphology of multiphase microstructures in steel. To perform the MPF simulation on an NVIDIA GPU, the program code is developed in CUDA Fortran. Using this code, the acceleration performance of the GPU implementation is evaluated, and our results demonstrate that the GPU computation can powerfully accelerate the MPF simulation by introducing an active parameter tracking (APT) method, which is used to reduce the computational load and memory consumption. The performance of the GPU computation with APT achieves a speedup factor of 5 compared with the GPU computation without APT and a speedup factor of 15 compared with the basic CPU computation without the APT method.

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Three-dimensional phase field modelling of the austenite-to-ferrite transformation

Cited by 123 publications

References 30 publications

Analysis of the Grain Size Evolution for Ferrite Formation in Fe-C-Mn Steels Using a 3D Model Under a Mixed-Mode Interface Condition

Analysis of the Grain Size Evolution for Ferrite Formation in Fe-C-Mn Steels Using a 3D Model Under a Mixed-Mode Interface Condition

Modelling Phase Transformation Kinetics in Fe–Mn Alloys

Multiphase Field Simulation of Austenite-to-Ferrite Transformation Accelerated by GPU Computing

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