Simulation of grain coarsening in two dimensions by cellular-automaton

Prediction of microstructural evolution during hot forging

“…However, Liu did not consider the probabilistic jumping of atoms in boundaries and boundary movement in the CA models. Geiger et al [73] developed a CA simulation model to investigate the effects of maximum orientation number, temperature and activation energy on grain growth kinetic by considering the atoms jumping in grain boundaries. Recently, by introducing the effect of the basic physical metallurgical principles, such as the thermodynamic driving mechanism, the curvature-driven mechanism and the lowest energy principle, into CA models, Raghavan et al [74, 75], He et al [76] and Chen et al [77] quantitatively simulated topological and kinetic features during normal grain growth.…”

“…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.…”

Cellular Automaton Modeling of Austenite Nucleation and Growth in Hypoeutectoid Steel during Heating Process

“…A variety of grain growth models were developed during past decades to study the grain size and the distribution of grain size during grain coarsening. [8][9][10][11][12][13] However, initial state of microstructure was set by using experimental data or sometimes invoking assumptions. Moreover, in most of the reported models, the transformation of ferrite into austenite was only assumed to be governed by the diffusion of carbon.…”

Monte Carlo Simulations of Grain Growth in Metals

“…By incorporating specific elements corresponding to various microstructural processes into the basic algorithm, the MC method has been adapted to model for instance grain growth in twophase materials (Holm et al, 1993) and composites (Miodownik et al, 2000), abnormal grain growth (Lee at al., 2000, Messina et al, 2001Ivasishin et al, 2004), static recrystallization (Srolovitz et al, 1986;Srolovitz et al, 1988;Rollett et al, 1992a, Rollett & Raabe, 2001Song & Rettenmayr, 2002)), dynamic recrystallization (Peczak, 1995;Rollett et al, 1992b) and sintering Chen et al, 1990, Matsubara, 1999, and it has been demonstrated that such MC simulations are capable of reproducing the essential features of these microstructural phenomena. Nowadays, the MC method is often preferred to deterministic methods such as cellular automaton (Geiger et al, 2001) and phase-field models (Tikare et al, 1998) at the mesoscopic level, mainly due to its inherent simplicity and flexibility. More recently, the MC method has also been employed to predict the final microstructures in engineering applications (Yang et al, 2000;.…”