A 3D Monte Carlo study of the effect of grain boundary anisotropy and particles on the size distribution of grains after recrystallisation and grain growth

Fjeldberg, Egil; Marthinsen, Knut

doi:10.1016/j.commatsci.2010.01.007

Cited by 26 publications

(12 citation statements)

References 22 publications

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“…Note that similar conclusions were presented in the first part of this work [22]. In [53,54,55,56], GB properties are defined as heterogeneous and not as anisotropic. The inclination dependency can have an important impact, hence, a complete description of the GB properties is necessary, i.e.…”

Section: Immersion Of Ebsd Datasupporting

confidence: 77%

“…Level-Set modeling of grain growth in 316L stainless steel under different assumptions regarding grain boundary properties 6.2 Current state of the modeling of 3D anisotropic grain growth A final question regarding the anisotropy of GB properties is still open: do the 3D description of GB properties can affect the microstructure evolution? Until now, most of the studies of GG in 3D have presented simulations of polycrystalline microstructures using different textures and a mathematical description of GB properties [53,54,55,56] or using data bases of GB energy [57,58]. The following conclusions are pointed out:…”

Section: Immersion Of Ebsd Datamentioning

confidence: 97%

“…the GB inclination is measured in the sample plane, and GB properties are simplified. Finally, regarding the study of GG in 3D, one frequently finds heterogeneous GB properties based on mathematical descriptors of GB properties [53,54,55,56] or based on databases of GB energy values [57,58]. Based on this, two open questions arise: can GB properties be described in 2D using the classical Read-Shockley [59] and Sigmoidal [60] model?…”

Section: Introductionmentioning

confidence: 99%

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Level-Set modeling of grain growth in 316L stainless steel under different assumptions regarding grain boundary properties

Murgas,

Flipon,

Bozzolo

et al. 2022

Preprint

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Two finite element level-set (FE-LS) formulations are compared for the modeling of grain growth of 316L stainless steel in terms of grain size, mean values and histograms. Two kinds of microstructures are considered, some are generated statistically from EBSD maps and the others are generated by immersion of EBSD data in the FE formulation. Grain boundary (GB) mobility is heterogeneously defined as a function of the GB disorientation. On the other hand, GB energy is considered as heterogeneous or anisotropic, respectively defined as a function of the disorientation and both the GB misorientation and the GB inclination. In terms of mean grain size value and grain size distribution (GSD), both formulations provide similar responses. However, the anisotropic formulation better respects the experimental disorientation distribution function (DDF) and predicts more realistic grain morphologies. It was also found that the heterogeneous GB mobility described with a sigmoidal function only affects the DDF and the morphology of grains. Thus, a slower evolution of twin boundaries (TBs) is perceived.

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Section: Immersion Of Ebsd Datasupporting

confidence: 77%

Section: Immersion Of Ebsd Datamentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Level-Set modeling of grain growth in 316L stainless steel under different assumptions regarding grain boundary properties

Murgas,

Flipon,

Bozzolo

et al. 2022

Preprint

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“…Otro sistema que también se ha simulado mediante el método Monte Carlo y que hace parte de las simulaciones con muestreo de importancia, es la microestructura de los materiales, un ejemplo es el crecimiento de granos en los materiales metálicos en donde se estudia por ejemplo la influencia de la anisotropía en las fronteras de grano y su movilidad a su vez como la distribución del tamaño de los mismo como se puede ver en la Figura 7 [45][46][47] o el estudio de la rugosidad en la interface de contacto entre dos tipos de materiales con estructura perovskita y de la forma La1/3Ca2/3MnO3 para el ferromagnético y La2/3Ca1/3MnO3 para el antiferromagnético [48]; esto se puede ver en la Figura 8 donde mediante Monte Carlo se genera todo el proceso de deposición de la primera capa con el material de comportamiento ferromagnético y luego se deposita la parte antiferromagnética y se observa el comportamiento y la influencia de la rugosidad en las propiedades magnéticas.…”

Section: Método De Monte Carlo De Muestreos Importantesunclassified

Fundamentos de simulación de materiales por medio del método de Monte Carlo

Hernández¹,

Flórez²

2014

Avances

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El método Monte Carlo es una herramienta que ha sido ampliamente utilizada desde sus inicios en la década de los años 1940 principalmente en la ciencia e ingeniería de materiales aunque es aplicable a temáticas tan diversas como la economía, la sociedad y su comportamiento, la biología y hasta la medicina. El funcionamiento del método Monte Carlo se basa en el uso de números aleatorios y en poder llegar a describir el comportamiento de un sistema o explicar un fenómeno difícil de comprender y de presentar analíticamente. Se pretende dar una introducción a los fundamentos básicos del método Monte Carlo aplicado a la ciencia de materiales como también mostrar algunos ejemplos basados en el algoritmo propuesto por N. Metropolis y que ha permitido abordar problemas interesantes en el tema. Al mismo tiempo se incluyen ejemplos básicos con sus propios algoritmos desarrollados en el lenguaje de programación FOR-TRAN 95 y que ilustran bastante bien las bases del método de Monte Carlo. Palabras claveAlgoritmo de Metropolis, Método Monte Carlo, Simulación computacional. AbstractMonte Carlo method is a tool that has been widely used in materials science and engineering since its Fundamentos de simulación de materiales por medio del método de Monte Carlo

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“…In conventional CA models, the assumption of misorientation-independent grain boundary mobility was made, so that the predicted topology features (size and side distribution) did not reproduce the log-normal distribution observed experimentally-instead the simulated topology distribution of grains appeared more left-skewed [31]. In fact, the grain boundary mobility is dependent on the misorientation and becomes anisotropic.…”

Section: Anisotropic Mobilitymentioning

confidence: 99%

Static coarsening of titanium alloys in single field by cellular automaton model considering solute drag and anisotropic mobility of grain boundaries

Yang

et al. 2012

Chin. Sci. Bull.

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Static coarsening is an important physical phenomenon that influences microstructural evolution and mechanical properties. How to simulate this process effectively has become an important topic which needs to be dealt with. In this paper, a new cellular automaton (CA) model, which considers the effect of solute drag and anisotropic mobility of grain boundaries, was developed to simulate static grain coarsening of titanium alloys in the beta-phase field. To describe the effect of the drag caused by different solute atoms on coarsening, their diffusion velocities in beta titanium were estimated relative to that of titanium atoms (Ti). A formula was proposed to quantitatively describe the relationship of the diffusion velocity of Ti to that of solute atoms; factors influencing the diffusion velocity such as solute atom radius, mass, and lattice type were considered. The anisotropic mobility of grain boundaries was represented by the parameter c 0 , which was set to 1 for a fully anisotropic effect. These equations were then implemented into the CA scheme to model the static coarsening of titanium alloys Ti-6Al-4V, Ti17 (Ti-5Al-4Mo-4Cr-2Sn-2Zr, wt%), TG6 (Ti-5.8Al-4.0Sn-4.0Zr-0.7Nb-1.5Ta-0.4Si-0.06C, wt%) and TA15 (Ti-6Al-2Zr-1Mo-1V, wt%) in the beta field. The predicted results, including coarsening kinetics and microstructural evolution, were in good agreement with experimental results. Finally, the effects of time, temperature, and chemical composition on grain coarsening and the limitations of the model were discussed. The mechanical properties of titanium alloys such as the creep resistance, fracture toughness and crack propagation resistance are determined by their grain sizes in the beta phase field [1,2]. However, the grains are prone to static coarsening when held in the beta phase field because of the structural characteristics of beta-titanium and relatively high stacking fault energy at high temperatures [3]; this results in a marked decrease in the mechanical properties of final products. Therefore, it is necessary to understand and to control static coarsening of titanium alloys in a single phase field to obtain the expected mechanical properties. Previously, many experimental investigations have focused on the static coarsening behaviors of titanium alloys in the beta phase field. Ivasishin et al. [4,5] investigated the effect of texture evolution on the values of the static coarsening exponent n and activation energy for Ti-6Al-4V alloy in the beta field. They found values of n ranging from 0.22 to 0.31 at different temperatures, but there were marked deviations attributed to different materials and experiments when the results were compared with those of others. Recently, Semiatin et al. [6][7][8] investigated the static grain coarsening of Ti-6Al-4V alloy via a series of heat treatments at typical temperatures. They found that the coarsening process of primary alpha particles was controlled by the volume diffusion of solute elements at lower temperatures and the growth exponent was equal to 0.33.

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A 3D Monte Carlo study of the effect of grain boundary anisotropy and particles on the size distribution of grains after recrystallisation and grain growth

Cited by 26 publications

References 22 publications

Level-Set modeling of grain growth in 316L stainless steel under different assumptions regarding grain boundary properties

Level-Set modeling of grain growth in 316L stainless steel under different assumptions regarding grain boundary properties

Fundamentos de simulación de materiales por medio del método de Monte Carlo

Static coarsening of titanium alloys in single field by cellular automaton model considering solute drag and anisotropic mobility of grain boundaries

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