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Modeling and Simulation of Dynamic Recrystallization Microstructure Evolution for GCr15 Steel Using the Level Set

Xuewen Chen1, Mingyang Liu1, Yisi Yang1

  • 1School of Materials Science and Engineering, Henan University of Science and Technology, Luoyang 471023, China.

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|January 25, 2025
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Summary

This study develops a model to predict dynamic recrystallization (DRX) in GCr15 bearing steel during hot deformation. The model accurately forecasts microstructural evolution, DRX fraction, and grain size, crucial for material performance.

Keywords:
GCr15 bearing steeldynamic recrystallizationlevel setmicrostructure evolution

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Area of Science:

  • Materials Science
  • Metallurgy
  • Computational Materials Science

Background:

  • Microstructure significantly impacts metallic material performance.
  • Understanding dynamic recrystallization (DRX) is key for controlling hot deformation processes.
  • GCr15 bearing steel's microstructure evolution requires accurate predictive models.

Purpose of the Study:

  • To establish a microstructural evolution model for DRX in GCr15 bearing steel.
  • To accurately predict DRX behavior and microstructural changes during hot deformation.
  • To integrate the model into finite element software for simulation.

Main Methods:

  • Combined Level Set (LS) method with Yoshie-Laasraoui-Jonas dislocation dynamics model.
  • Conducted hot compression tests on GCr15 steel using a thermal simulator.
  • Derived material parameters from experimental flow stress data and integrated into DIGIMU® software.

Main Results:

  • Investigated effects of temperature, strain, and strain rate on DRX and grain size.
  • Successfully simulated GCr15 steel DRX microstructure during hot compression.
  • Predicted mean grain size and flow stress showed excellent agreement with experimental results.

Conclusions:

  • The developed DRX model effectively predicts the evolution of DRX fraction and average grain size.
  • The model reliably forecasts DRX behavior during hot forging processes.
  • Accurate prediction of microstructural evolution enhances control over material performance.