薄规格取向硅钢二次再结晶过程中Goss织构演变的Monte Carlo模拟

doi:10.11896/j.issn.1005-023X.2018.02.030

Abstract
Figure/Table
References
Related Citation (2)

Download:

richhtml

Full Text ( PDF ) (2170KB)
Export: BibTeX | EndNote (RIS)

Abstract

The orientation data of surface grains of the primary recrystallized samples of thin gauge grain oriented silicon steel (0.18 mm thick) were obtained by EBSD experiment and the initial microstructure were generated from the orientation data. The Potts model Monte Carlo method was used to simulate the secondary recrystallization of thin gauge graded grain oriented silicon steel, and the effect of surface energy on the evolution of Goss texture was studied. The simulation results show that the surface energy difference between Goss grain and its adjacent grains is an important driving force for Goss grain growth. The surface energy diffe-rence has a critical value (≈12%), only when the surface energy difference is larger than the critical value, can the surface energy-driven abnormal Goss grain growth occur.

Key words： thin gauge grain oriented silicon steel Monte Carlo simulation Goss texture

Published: 25 January 2018

CLC number:

TG142.77

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Guang MA
	Xin CHEN
	Licheng LU
	Dongqun XIN
	Li MENG
	Hao WANG
	Ling CHENG
	Fuyao YANG

Cite this article:

Guang MA,Xin CHEN,Licheng LU, et al. Monte Carlo Simulation of the Evolution of Goss Texture in Secondary Recrystallization of Thin Gauge Grain Oriented Silicon Steel[J]. Materials Reports, 2018, 32(2): 313-315.

URL:

http://www.mater-rep.com/EN/10.11896/j.issn.1005-023X.2018.02.030 OR http://www.mater-rep.com/EN/Y2018/V32/I2/313

1	1 毛卫民, 杨平 . 电工钢的材料学原理[M]. 北京: 高等教育出版社, 2013.
2	Harase J, Shimizu R, Dingley D J . Texture evolution in the presence of precipitates in Fe-3% Si alloy[J]. Acta Metallurgica Et Materialia, 1991,39:763.
3	Lin P, Palumbo G, Harase J , et al. Coincidence site lattice (CSL) grain boundaries and Goss texture development in Fe-3% Si alloy[J]. Acta Metallurgica Et Materialia, 1996,44(12):4677.
4	Harase J, Shimizu R, Kim J K , et al. The role of high energy boundaries and coincidence boundaries in the secondary recrystallization of grain-oriented silicon steel[J]. Metals and Materials International, 1999,5(5):429.
5	Hayakawa Y, Szpunar J A, Palumbo G , et al. The role of grain boundary character distribution in Goss texture development in electrical steels[J]. Journal of Magnetism and Magnetic Materials, 1996,160(4):143.
6	Hayakawa Y, Szpunar J A . A new model of Goss texture development during secondary recrystallization of electrical steel[J]. Acta Materialia, 1997,45(11):4713.
7	Zhang L Z, Rollett A D, Bartel T , et al. A calibrated Monte Carlo approach to quantify the impacts of misorientation and different driving forces on texture development[J]. Acta Materialia, 2012,60(3):1201.
8	Maazi N . Introduction of preferential interaction particle-grain boundary in grain growth simulation—Application to the abnormal Goss grain growth in the Fe-3%Si magnetic alloys[J]. Computatio-nal Materials Science, 2013,79:303.
9	Liu Y, Baudin T, Penelle R . Simulation of normal grain growth by cellular automata[J]. Scripta Materialia, 1996,34(11):1679.
10	Plimpton S . Fast parallel algorithms for short-range molecular dynamics[J]. Journal of Computational Physics, 1995,117(1):1.
11	CEK Iii, Chen L Q . Computer simulation of 3-D grain growth using a phase-field model[J]. Acta Materialia, 2002,50(12):3059.
12	Anderson M P, Srolovitz D J, Grest G S , et al. Computer simulation of grain growth-Ⅰ. Kinetics[J]. Acta Metallurgica, 1984,32(5):783.
13	Srolovitz D J, Anderson M P, Sahni P S , et al. Computer simulation of grain growth-Ⅱ. Grain size distribution, topology, and local dynamics[J]. Acta Metallurgica, 1984,32(5):793.
14	Radhakrishnan B, Zacharia T . Simulation of curvature-driven grain growth by using a modified monte carlo algorithm[J]. Metallurgical and Materials Transactions A, 1995,26(1):167.
15	Song X, Liu G, He Y . Modified Monte Carlo method for grain growth simulation[J]. Progress in Natural Science, 1998,8(1):92.
16	Wang Hao, Liu Guoquan, Qin Xiangge . Simulation of 3D grain growth from different initial states and their quasi-stationary state distributions[J]. Rare Metal Materials and Engineering, 2009,38(1):126(in Chinese).
17	王浩, 刘国权, 秦湘阁 . 不同初始组织的3D晶粒长大仿真及其准稳态分布研究[J]. 稀有金属材料与工程. 2009,38(1):126.
18	Meng L, Wang H, Liu G Q, Chen Y . Study on topological properties in two-dimensional grain networks via large-scale Monte Carlo simulation[J]. Computational Materials Science, 2015,103:165.