留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码
Volume 17 Issue 6
Dec.  2010
数据统计

分享

计量
  • 文章访问数:  236
  • HTML全文浏览量:  42
  • PDF下载量:  10
  • 被引次数: 0
Zhen Wang, Xue-feng Liu, Yong He,  and Jian-xin Xie, A fractal-based model for the microstructure evolution of silicon bronze wires fabricated by dieless drawing, Int. J. Miner. Metall. Mater., 17(2010), No. 6, pp. 770-776. https://doi.org/10.1007/s12613-010-0387-4
Cite this article as:
Zhen Wang, Xue-feng Liu, Yong He,  and Jian-xin Xie, A fractal-based model for the microstructure evolution of silicon bronze wires fabricated by dieless drawing, Int. J. Miner. Metall. Mater., 17(2010), No. 6, pp. 770-776. https://doi.org/10.1007/s12613-010-0387-4
引用本文 PDF XML SpringerLink

A fractal-based model for the microstructure evolution of silicon bronze wires fabricated by dieless drawing

  • 通讯作者:

    Jian-xin Xie    E-mail: jxxie@mater.ustb.edu.cn

  • The back-propagation neural (BPN) network was proposed to model the relationship between the parameters of the dieless drawing process and the microstructures of the QSi3-1 silicon bronze alloy. Combined with image processing techniques, grain sizes and grain-boundary morphologies were respectively determined by the quantitative metallographic method and the fractal theory. The outcomes obtained show that the deformed microstructures exhibit typical fractal features, and the boundaries can be characterized quantitatively by fractal dimensions. With the temperature of 600–800℃ and the drawing speed of 0.67–1.00 mm·s-1, either a lower temperature or a higher speed will cause a smaller grain size together with an elevated fractal dimension. The developed model can be capable for forecasting the microstructure evolution with a minimum error. The average relative errors between the predicted results and the experimental values of grain size and fractal dimension are 3.9% and 0.9%, respectively.
  • A fractal-based model for the microstructure evolution of silicon bronze wires fabricated by dieless drawing

    + Author Affiliations
    • The back-propagation neural (BPN) network was proposed to model the relationship between the parameters of the dieless drawing process and the microstructures of the QSi3-1 silicon bronze alloy. Combined with image processing techniques, grain sizes and grain-boundary morphologies were respectively determined by the quantitative metallographic method and the fractal theory. The outcomes obtained show that the deformed microstructures exhibit typical fractal features, and the boundaries can be characterized quantitatively by fractal dimensions. With the temperature of 600–800℃ and the drawing speed of 0.67–1.00 mm·s-1, either a lower temperature or a higher speed will cause a smaller grain size together with an elevated fractal dimension. The developed model can be capable for forecasting the microstructure evolution with a minimum error. The average relative errors between the predicted results and the experimental values of grain size and fractal dimension are 3.9% and 0.9%, respectively.
    • loading

    Catalog


    • /

      返回文章
      返回