Self-consistent clustering analysis for multiscale modeling at finite strains

Cheng Yu, Orion L. Kafka, Wing Kam Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Accurate and efficient modeling of microstructural interaction and evolution for prediction of the macroscopic behavior of materials is important for material design and manufacturing process control. This paper approaches this challenge with a reduced-order method called self-consistent clustering analysis (SCA). It is reformulated for general elasto-viscoplastic materials under large deformation. The accuracy and efficiency for predicting overall mechanical response of polycrystalline materials is demonstrated with a comparison to traditional full-field solution methods such as finite element analysis and the fast Fourier transform. It is shown that the reduced-order method enables fast prediction of microstructure–property relationships with quantified variation. The utility of the method is demonstrated by conducting a concurrent multiscale simulation of a large-deformation manufacturing process with sub-grain spatial resolution while maintaining reasonable computational expense. This method could be used for microstructure-sensitive properties design as well as process parameters optimization.

Original languageEnglish (US)
Pages (from-to)339-359
Number of pages21
JournalComputer Methods in Applied Mechanics and Engineering
Volume349
DOIs
StatePublished - Jun 1 2019

Keywords

  • Concurrent multiscale
  • Data-driven
  • Material design
  • Polycrystal plasticity
  • Process–structure–property
  • Reduced-order modeling

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications

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