Journal of Materials Science

, Volume 48, Issue 16, pp 5517–5527 | Cite as

3D modelling of ferrite and austenite grain coarsening using real-valued cellular automata based on transition function

  • Ye. VertyaginaEmail author
  • M. Mahfouf
  • X. Xu


A new extended technique for 3D modelling of normal grain growth in low carbon steels is presented in this paper. This technique is based on real-valued cellular automata with the use of a local transition function that allows it to be applied to materials with both fcc and bcc lattices with the grain growth being easily simulated in ferrite as well as in austenite cases. The simulated data were calibrated with four sets of experimental data for isothermal grain coarsening in austenite, alpha- and delta-ferrites. The obtained results cogently demonstrate that there is a good agreement between simulated and experimental data across a wide range of temperatures. The new model developed in this paper, allows for the identification of two different mechanisms of grain growth in austenite. It is also shown in this paper that the newly presented approach can be used to extract additional parameters from the grain growth process, such as grain boundary velocity, mobility and driving force, which are hardly accessible even via real-time experiments.


Ferrite Austenite Cellular Automaton Cellular Automaton Lattice Diffusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to acknowledge the UK-Engineering and Physical Sciences Research Council (UK-EPSRC) for their financial support under Grant No. EP/F023464/1. The authors would also like to thank Professor A. Howe (Tata Steel), Dr C. Pinna, Dr Y. Lan, and Professor P. Tsakiropoulos for their helpful comments during the course of this research work. Finally, they wish to thank the Editor and the anonymous reviewers for their comments which helped to improve the quality of this paper.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.University of OxfordOxfordUK
  2. 2.Department of ACSEThe University of SheffieldMappin streetUK
  3. 3.Sheffield Hallam UniversitySheffieldUK

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