An introductory review of cellular automata modeling of moving grain boundaries in polycrystalline materials

Janssens, Maddy

doi:10.1016/j.matcom.2009.02.011

Cited by 100 publications

(38 citation statements)

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“…The grain growth kinetic equation has also been established by Beck et al [70]. Experimental results suggest that the grain-size distribution is approximately log-normal for normal grain growth [71,72].…”

Section: Ca Model For Normal Grain Growthmentioning

confidence: 88%

“…These features indicate that it is possible to model the microstructure evolution within a unified frame [64][65][66]. In contrast to the limited applicability of the macro-scale models, e.g., the phenomenological model and the statistical model, this characteristic of mesoscopic models also shows the latent advantage of the numerical solution of the complexity of microstructural evolution globally , such as normal grain growth [68][69][70][71][72][73][74][75][76], recrystallization [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98] and phase transformation [100][101][102][103]. 4.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of microstructural evolution during hot forging

2014

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-Microstructural evolution, which is governed by temperature, strain and strain rate during hot forging, is a key factor influencing mechanical properties. Understanding the microstructural evolution of metals and alloys in hot forging has a great importance for the designers of metal forming processes. The principal objective of this paper is to provide an overview of the models for the prediction of microstructural evolution for metals and alloys during the hot forging process. In this review paper, the models are divided into four categories, including the phenomenological, physically-based, mesoscale and artificial-neural-network models, to introduce their developments, prediction capabilities and application scopes. Additionally, some limitations and objective suggestions for the further development of the modelling of microstructural evolution during hot forging are proposed.

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Section: Ca Model For Normal Grain Growthmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Prediction of microstructural evolution during hot forging

2014

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“…However, the location of the interface can be obtained by determining the cells where the solid fraction varies from 0 to 1. As a result, the CA method can capture complex interfacial dynamics with great efficiency and its computational requirements (memory and time) are much lower than other methods such as phase-field methods [19]. A review of CA modeling of microstructural evolution has been presented by Janssens [19] and He et al [20].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the CA method can capture complex interfacial dynamics with great efficiency and its computational requirements (memory and time) are much lower than other methods such as phase-field methods [19]. A review of CA modeling of microstructural evolution has been presented by Janssens [19] and He et al [20]. It has been used for modeling structural evolution during solidification [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Cellular Automata Simulation of Gap-Filler Dissolution during Transient Liquid Phase Bonding of Single Crystal Materials

Ghoneim¹,

Ojo²

2012

MNSMS

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A new numerical model is developed using a Cellular Automata (CA) method to study the liquid-phase dissolution behavior of gap-filler powder particles in interlayer powder mixture during transient liquid phase (TLP) bonding process. The model prediction of microstructural evolution in TLP joint between single crystal substrates show that formation of misoriented stray-grains results from incomplete liquation of the gap-filler powder particles. In contrast to what is generally assumed and reported, numerical calculations coupled with experimental verification show that under properly selected process parameters, complete melting of the gap-filler powder particles is possible. This is imperative to prevent the formation of misoriented stray-grains and maintain single crystallinity during TLP bonding of single crystal materials. The dependence of complete melting of the gap-filler particles on salient TLP bonding parameters are analyzed and discussed.

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“…A basic feature of this method is to distribute nodes randomly in the domain instead of using regular cells, which leads to different distances between the nodes and different neighborhood configurations for each of them. This new approach was first proposed by Janssens for modelling the re-crystallisation (Janssens, 2000(Janssens, , 2003(Janssens, , 2010Raabe et al, 2007). were the first to couple the classical CA method with a meshless method instead of the FEM or FDM.…”

mentioning

confidence: 99%