Mesoscale Modeling of Dynamic Recrystallization: Multilevel Cellular Automaton Simulation Framework
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UCTION
DYNAMIC recrystallization (DRX), as the main mechanism for the evolution of metal microstructure, is considered to be an effective method for grain refinement during thermoplastic deformation. New grains and grain refinement during the DRX process play critical roles in affecting the load, microstructure, and forming quality of products in the hot working process. However, there is still lack of mesoscopic scale models based on physical metallurgy theory in predicting the evolution of microstructure (e.g., the inoculation of nucleation, grain growth, and topological change of the matrix) and corresponding effects on the macroscopic
FEI CHEN, HUAJIA ZHU, HAIMING ZHANG, and ZHENSHAN CUI are with the National Engineering Research Center of Die and Mold CAD, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, P.R. China. Contact e-mails: [email protected], [email protected] Manuscript submitted September 30, 2019. Fei Chen and Huajia Zhu contributed equally to this work.
METALLURGICAL AND MATERIALS TRANSACTIONS A
flow behavior of engineering materials such as stainless steel, titanium alloy, and Ni-based superalloys.[1–7] Therefore, establishing the physical-based mesoscale method of DRX is of critical importance in both academia and industry. The microstructural mechanisms and modeling methods of DRX for engineering metals and alloys have been extensively investigated in the literature. Traditional experimental techniques such as optical microscope (OM),[8–10] electron backscatter diffraction (EBSD),[11,12] scanning electron microscope (SEM),[13,14] and transmission electron microscope (TEM)[15,16] have been widely used to study DRX behavior from macroscale to nanoscale for metals and alloys. However, the process of nucleation and grain growth in the DRX process cannot be predicted and tracked by using these methods. Besides, some researchers have studied the dynamic behavior of the DRX process by constructing empirical and theoretical models, including phenomenological models based on empirical observations and mathematical functions and internal state variables (ISV) models. Phenomenological models[17–23] can predict the stress–strain curves and the final microstructure (grain size, etc.). However, it is based
on empirical observations, and the physical mechanism of DRX in these methods is insufficient, for example, the nucleation law and the grain boundary (GB) migration driven by the energy storage difference are hard to be considered. Furthermore, these methods have a relatively limited ability to describe the dynamic mechanism of DRX and visually describe the evolution process of microstructure. Another limitation of these models is that they are not easy to be applied to other materials or to more complex DRX processes. The internal state variable (ISV) model[1,24–32] incorporates internal state variables (such as dislocation density, grain size, etc.) into the constitutive equation. It is easy to implement and has good computational efficiency. However, it is difficult to develop a model that c
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