谷歌浏览器插件
订阅小程序
在清言上使用

Kinetic Monte Carlo Simulation of Abnormal Grain Growth in Textured Systems with Anisotropic Grain Boundary Energy and Mobility

MATERIALS TODAY COMMUNICATIONS(2022)

引用 7|浏览5
暂无评分
摘要
The abnormal grain growth behavior of an individual grain in the textured system was simulated by kinetic Monte Carlo method with focusing on the effect of anisotropy configuration and texture intensity. The mobility anisotropy and the energy anisotropy of grain boundary were derived based on the theoretical and experimental data in Fe-Si alloy. The results indicated that the single configured system with anisotropic grain boundary mobility or energy led to apparently unreasonable abnormal growth kinetic and boundary evolution at the growing front of the candidate grains. The composite configured system with anisotropic grain boundary mobility and energy accurately reproduced the microstructure evolution during abnormal grain growth. The 20 degrees - 45 degrees boundaries at the growing front varied with a certain range in the textured systems. The diameter of the abnormal grain increased linearly with the time, and the growth rate decreased with the weakening of matrix texture intensity. It is found that the grain boundary energy anisotropy plays the key role in the system with relatively strong matrix texture, while the grain boundary mobility anisotropy plays the key role in the system with random matrix texture. By converting the simulation time to the real time, the abnormal grain growth rate is estimated to be similar to 1188.5-996.5 mu m/min, which is roughly consistent with the actual secondary recrystallization. The present simulation is helpful to understand the abnormal grain growth phenomenon, especially the Goss grain growth in grain-oriented electrical steel. Data availability: The data required to reproduce these findings cannot be shared at this time since the data forms part of an ongoing study.
更多
查看译文
关键词
Kinetic Monte Carlo simulation, Abnormal grain growth, Matrix texture, Grain boundaries, Microstructure
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要