J. Mater. Sci. Technol. ›› 2010, Vol. 26 ›› Issue (12): 1063-1070.

• Research Articles • 上一篇    下一篇

原始晶粒尺寸对动态再结晶过程的影响

金朝阳1,崔振山2   

  1. 1. 上海交通大学
    2. 上海华山路1954号上海交通大学模具所
  • 收稿日期:2009-06-12 修回日期:2009-11-09 出版日期:2010-12-31 发布日期:2010-12-21
  • 通讯作者: 金朝阳
  • 基金资助:

    国家重点基础研究发展计划（973）项目

Modelling the Effect of Initial Grain Size on Dynamic Recrystallization Using a Modified Cellular Automata and a Adaptive Response Surface Method

Zhaoyang Jin, Zhenshan Cui   

  1. 1) National Die and Mold CAD Engineering Research Center, Shanghai Jiao Tong University, Shanghai 200030, China
    2) School of Mechanical Engineering, Yangzhou University, Yangzhou 225001, China
  • Received:2009-06-12 Revised:2009-11-09 Online:2010-12-31 Published:2010-12-21
  • Contact: Zhao-Yang JIN
  • Supported by:

    the National Basic Research Program of China (No. 2006CB705401), the National Natural Science Foundation of China (No. 51075270) and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 10KJD460003)

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摘要： 应用元胞自动机（CA）模型定量分析了原始晶粒尺寸对动态再结晶（DRX）微观组织演化和流动应力行为的影响。为使CA模拟更接近于实际的DRX过程，在CA模型中考虑了变形程度对DRX形核行为的影响。形核模型中的参数由基于材料流变应力的参数识别方法确定，该方法通过将DRX的CA模型与基于自适应响应面模型（ARSM）的优化技术相结合而实现。以高导无氧铜为例，讨论了原始晶粒尺寸对DRX行为的影响。模拟结果与实验结果的良好一致性验证了本文所建立的CA模型及其参数识别方法是有效的。此外，研究结果表明原始晶粒尺寸改变动态再结晶动力学与流变曲线形状，但是不影响稳态晶粒尺寸和稳态应力。

关键词: 动态再结晶, 形核模型, 元胞自动机方法, 响应面方法, 参数识别

Abstract: A modified cellular automata (CA) model of dynamic recrystallization (DRX) and a flow stress-based nucleation parameter identification method have been developed. In the method, the modified CA model, which takes the role of deformation degree on nucleation behavior into consideration, is coupled with an adaptive response surface model (ARSM) to search for the optimum nucleation parameter. The DRX behavior of an oxygen free high conductivity (OFHC) copper with different initial grain sizes has been taken as an example to validate the model. Good agreement is found between the simulated and the experimental results, which demonstrates that the new method can effectively improve the simulation accuracy.

Key words: Dynamic recrystallization, Cellular automata method, Nucleation model, Response surface method, Parameter identification