Combined Cellular Automaton Model for Dynamic Recrystallization Evolution of 42CrMo Cast Steel
Tóm tắt
The dynamic recrystallization (DRX) simulation performance largely depends on simulated grain topological structures. However, currently solutions used different models for describing two-dimensional (2D) and three-dimensional (3D) grain size distributions. Therefore, it is necessary to develop a more universal simulation technique. A cellular automaton (CA) model combined with an optimized topology deformation technology is proposed to simulate the microstructural evolution of 42CrMo cast steel during DRX. In order to obtain values of material constants adopted in the CA model, hot deformation characteristics of 42CrMo cast steel are investigated by hot compression metallographic testing. The proposed CA model deviates in two important aspects from the regular CA model. First, an optimized grain topology deformation technology is utilized for studying the hot compression effect on the topology of grain deformation. Second, the overlapping grain topological structures are optimized by using an independent component analysis method, and the influence of various thermomechanical parameters on the nucleation process, grain growth kinetics, and mean grain sizes observed during DRX are explored. Experimental study shows that the average relative root mean square error (RRMSE) of the mean grain diameter obtained by the regular CA model is equal to 0.173, while the magnitude calculated using the proposed optimized CA model is only 0.11. This paper proposes a novel combined CA model for simulating the microstructural evolution of 42CrMo cast steel, which notably uses a ICA-based grain topology deformation method to optimize the overlapping grain topological structures in simulation.
Tài liệu tham khảo
F Chen, Z S Cui, J Liu, et al. Mesoscale simulation of the high-temperature austenitizing and dynamic recrystallization by coupling a Cellular Automaton with a topology deformation technique. Materials Science and Engineering A, 2010, 527(21): 5539–5549.
R D MacPherson, D J Srolovitz. The von Neumann relation generalized to coarsening of three-dimensional microstructures. Nature, 2007, 446(7139): 1053–1055.
H Wang, G Liu. Study of 3D quasi-stationary grain size distribution derived from macpherson-srolovitz topology-related grain growth rate equation. Acta Metallurgica Sinica, 2008, 44(7): 769–774. (in Chinese)
C S Pande, K P Cooper. Self-similar grain size distribution in two dimensions: Analytical solution. Acta Materialia, 2008, 56(16): 4200–4205.
C S Pande, K P Cooper. On the analytical solution for self-similar grain size distributions in two dimensions. Acta Materialia, 2011, 59(3): 955–961.
J C Tucker, L H Chan, G S Rohere, et al. Comparison of grain size distributions in a Ni-based superalloy in three and two dimensions using the Saltykov method. Scripta Materialia, 2012, 66(8): 554–557.
V P R M Beers, V G Kouznetsova, M G D Geers, et al. A multiscale model of grain boundary structure and energy: From atomistics to a continuum description. Acta Materialia, 2015, 82: 513–529.
S Keshavarz, S Ghosh. Hierarchical crystal plasticity FE model for nickel-based superalloys: Sub-grain microstructures to polycrystalline aggregates. International Journal of Solids and Structures, 2015, 55: 17–31.
H Aapo, K Juha, O Erkki. Independent component analysis. New York: John Wiley & Sons, 2001.
Y Guo, S Huang, Y Li, et al. Edge effect elimination in single-mixture blind source separation. Circuits, Systems, and Signal Processing, 2013, (32)5: 2317–2334.
Y Guo, S Huang, Y Li. Single-mixture source separation using dimensionality reduction of ensemble empirical mode decomposition and independent component analysis. Circuits, Systems, and Signal Processing, 2012, 31(6): 2047–2060.
Y Guo, S Ding, Y Li, et al. Multiscale modeling for 42CrMo ring during blank-casting and rolling compound forming process. Journal of Mechanical Engineering, 2014, 50(14): 81–88. (in Chinese)
R Ding, Z X Guo. Microstructural modelling of dynamic recrystallisation using an extended cellular automaton approach. Computational Materials Science, 2002, 23(1): 209–218.
Chen, K Qi, Z Cui, et al. Modeling the dynamic recrystallization in austenitic stainless steel using cellular automaton method. Computational Materials Science, 2014, 83: 331–340.
W Roberts, B Ahlblom. A nucleation criterion for dynamic recrystallization during hot working. Acta Metallurgica, 1978, 26(5): 801–813.
Liu, B W Zhu, L X Li. Dynamic recrystallization of AZ31 magnesium alloysimulated by LaasraouiJonas dislocation equation coupled cellular automata method. The Chinese Journal of Nonferrous Metals, 2013, 23(4): 898–904.
F A Hua, Y S Yang, D Y Guo, et al. A grain growth cellular automata model based on the curvature-driven mechanism. Acta Metallurgica Sinica, 2004, 40(11): 1210–1214.
X J Guan, X Y Jiao, J J Zhou, et al. Cellular automata simulation of single grain growth. The Chinese Journal of Nonferrous Metals, 2007, 17(5): 699–703.
Guo, Y Li, Z Guo, et al. Microstructural evolution of as-cast 42CrMo ring during hot rolling. Journal of Mechanical Engineering, 2014, 50(12): 30–35. (in Chinese)
F Qin, Y Li, H Qi, et al. Deformation behavior and microstructure evolution of as-cast 42CrMo alloy in isothermal and non-isothermal compression. Journal of Materials Engineering and Performance, 2016, 25(11): 5040–5048.
G Kugler, R Turk. Modeling the dynamic recrystallization under multi-stage hot deformation. Acta Materialia, 2004, 52(15): 4659–4668.