Crack initiation and fatigue life prediction on aluminum lug joints using statistical volume element-based multiscale modeling

Jinjun Zhang, Kuang Liu, Chuntao Luo, Aditi Chattopadhyay

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

This article presented the application of an energy-based multiscale damage criterion for crack initiation and life prediction in crystalline metallic aerospace structural components under fatigue loading. A novel meso statistical volume element model was developed to improve computational efficiency compared to traditional meso representative volume element models. The key microscale factors affecting the mechanical properties of crystalline materials, including grain orientation, misorientation, principal axis direction, size, aspect ratio, and shape were considered in the formation of the statistical volume element model. The effect of several factors was studied to assess the importance in the overall macroscopic response of the material. Fatigue tests of lug joint samples were performed to validate the damage criterion as well as the statistical volume element model. Crack initiation was predicted within 29% accuracy, and orientation was predicted within a 2 range, which was comparable to other methods. The simulation efficiency of the statistical volume element model was improved 15 times over the traditional representative volume element models.

Original languageEnglish (US)
Pages (from-to)2097-2109
Number of pages13
JournalJournal of Intelligent Material Systems and Structures
Volume24
Issue number17
DOIs
StatePublished - Nov 1 2013

Keywords

  • Structural health monitoring
  • multiscale modeling
  • statistical volume element

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanical Engineering

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