FEM-based modeling of dynamic recrystallization of AISI 52100 steel using cellular automaton method

K. L. Wang, Mingwang Fu, J. Lu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

The dynamic recrystallization (DRX) behavior in the isothermal hot compression of AISI 52100 steel was analyzed by using the phenomenological-based cellular automaton (CA) algorithm. The developed CA model was coded into DEFORM platform, which is a Finite Element Method (FEM)-based software for simulation of material deformation process. The developed CA-model can thus predict the nucleation and growth kinetics of dynamically recrystallized grains of the testing material in hot working process. Furthermore, the effects of the deformation temperature, true strain and strain rate on the microstructural evolution of the testing material were physically studied by using Gleeble-1500 thermo-mechanical simulator and the developed CA-model was verified by the experimental results. Through simulation and experiment, it is found that the results predicted by the CA-model have a good agreement with the experimental ones.
Original languageEnglish
Title of host publicationAdvances in Precision Engineering
Pages406-411
Number of pages6
DOIs
Publication statusPublished - 29 Dec 2010
EventICoPE2010 and 13th ICPE International Conference on Precision Engineering - , Singapore
Duration: 28 Jul 201030 Jul 2010

Publication series

NameKey Engineering Materials
Volume447 448
ISSN (Print)1013-9826

Conference

ConferenceICoPE2010 and 13th ICPE International Conference on Precision Engineering
Country/TerritorySingapore
Period28/07/1030/07/10

Keywords

  • AISI 52100 steel
  • Cellular automaton
  • Dynamic recrystallization

ASJC Scopus subject areas

  • General Materials Science
  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'FEM-based modeling of dynamic recrystallization of AISI 52100 steel using cellular automaton method'. Together they form a unique fingerprint.

Cite this