A New Cellular Automaton Method Coupled with a Rate-dependent (CARD) Model for Predicting Dynamic Recrystallization Beha

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THE hot deformation behavior of alloys is a complicated process, which has been widely studied. Among the metallurgical phenomena, dynamic recrystallization (DRX) is one of the key processes controlling the grain size of alloys during the hot plastic deformation, especially in metals with low or medium levels of stacking fault energy.[1,2] In order to accurately control the microstructure, it is important to predict the DRX behavior and microstructure development of alloys during hot deformation. The topology of the microstructure (shown by an image illustrating the arrangement of grains) and microstructural characteristics (grain size, fraction of DRX area, etc.) of alloys can be characterized by a discrete mesoscale modeling of the microstructure. Various approaches have been utilized for modeling microstructural evolutions during the recrystallization phenomenon, such as Monte Carlo,[3–5] vertex,[6,7] phase field,[8,9] and single grain approaches.[10,11] Among these models, the cellular automaton (CA) model is most widely utilized,[12,13] owing to its capacity to generate

M. AZARBARMAS and M. AGHAIE-KHAFRI are with the Faculty of Materials Science and Engineering, K.N. Toosi University of Technology, Tehran, Iran. Contact e-mails: [email protected]; [email protected]. Manuscript submitted May 25, 2017

METALLURGICAL AND MATERIALS TRANSACTIONS A

complex time–space patterns according to very simple rules.[14,15] The CA model can successfully predict the growth kinetics based on grain boundary mobility,[16] or the dendrite tip kinetics model.[17] In a CA model, the state of a cell is determined by considering the state of its neighboring cells and transition rules. The CA approach has been widely used to evaluate the static[18–23] and dynamic[24,25] recrystallization of alloys. Azarbarmas and Aghaie-Khafri have shown that a CA approach can model the DRX process more accurately than the conventional method.[25] Recently, Zhang et al.[24] used the CA approach to model the DRX phenomenon during the hot deformation of the 7055 aluminum alloy, and to optimize the deformation parameters. Sitko et al.[26] also evaluated the CA model for estimating DRX behavior. They showed that there is a minimum time step threshold and a minimum resolution of the CA space for producing reasonable results. Jin et al.[27] presented an approach for microstructural modeling during the DRX of copper using a CA model to quantitatively predict the microstructural features. They utilized an adaptive response surface method as an optimization model to provide the input parameters of the CA model. Recently, Li et al.[28] and Haipeng et al.[29] examined the influences of process parameters on the fraction and size of DRX grains using the CA approach, and showed that the predicted results were in good agreement with the experimental data. One of the advantages of CA model is its ability to couple with other models. Xiao et al.[30] used a CA model coupled with a topology deformation technique,

while Raabe and Becker,[31] Li et al.,[32]