An autonomous surface discontinuity detection and quantification method by digital image correlation and phase congruency

Ahmet Cinar, S. M. Barhli, Mathias Flansbjer, David Hollis, Rachel Tomlinson, James Marrow, Mahmoud Mostafavi*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

28 Citations (Scopus)
338 Downloads (Pure)

Abstract

Digital image correlation has been routinely used to measure full-field displacements in many areas of solid mechanics, including fracture mechanics. Accurate segmentation of the crack path is needed to study its interaction with the microstructure and stress fields, and studies of crack behaviour, such as the effect of closure or residual stress in fatigue, require data on its opening displacement. Such information can be obtained from any digital image correlation analysis of cracked components, but it collection by manual methods is quite onerous, particularly for massive amounts of data. We introduce the novel application of Phase Congruency to detect and quantify cracks and their opening. Unlike other crack detection techniques, Phase Congruency does not rely on adjustable threshold values that require user interaction, and so allows large datasets to be treated autonomously. The accuracy of the Phase Congruency based algorithm in detecting cracks is evaluated and compared with conventional methods such as Heaviside function fitting. As Phase Congruency is a displacement-based method, it does not suffer from the noise intensification to which gradient-based methods (e.g. strain thresholding) are susceptible. Its application is demonstrated to experimental data for cracks in quasi-brittle (Granitic rock) and ductile (Aluminium alloy) materials.

Original languageEnglish
Pages (from-to)94-106
Number of pages13
JournalOptics and Lasers in Engineering
Volume96
Early online date4 May 2017
DOIs
Publication statusPublished - Sep 2017

Keywords

  • Digital image correlation
  • fracture mechanics
  • crack
  • phase congruency
  • segmentation

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