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Static recrystallization study on pure aluminium using crystal plasticity finite element and phase-field modelling

  • Qinmeng Luan (Corresponding Author)
  • , Junyi Lee
  • , Zebang Zheng
  • , Jianguo Lin
  • , Jun Jiang

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

Abstract

In-depth understanding of the recrystallization process in alloys is critical for generating desirable small grains and weak textured microstructure, which provides high strength and toughness for metal formed parts. The manufacturing industry has a high demand for a valid computational model to accurately predict the level of recrystallization and recrystallized grain size under different strain paths and temperatures. However, current understanding and numerical calculation have not been linked properly for a reliable, physically based model to simulate the deformation and annealing process. Our phase-field model coupled with crystal plasticity simulations, which is based on the theory of stored energy minimization, enables a reliable prediction on the microstructure evolution under different processing routes. We hope that this modelling work provides a solution for the prediction of some long standing microstructure formation problems.

Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Metal Forming METAL FORMING 2018
Pages1800-1807
Number of pages8
Volume15
DOIs
Publication statusPublished - Sept 2018
Externally publishedYes
Event17th International Conference on Metal Forming, METAL FORMING 2018 - Toyohashi, Japan
Duration: 16 Sept 201819 Sept 2018

Publication series

NameProcedia Manufacturing
PublisherElsevier BV

Conference

Conference17th International Conference on Metal Forming, METAL FORMING 2018
Country/TerritoryJapan
CityToyohashi
Period16/09/1819/09/18

Keywords

  • Crystal plastic finite element model
  • Phase-field model
  • Pure aluminium
  • Static recrystallization

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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