Automatic Microstructure Defect Detection of Ti-6Al-4V Titanium Alloy by Regions-Based Graph

Ruoxu Ren, Terence Hung, Kay Chen Tan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


In this paper, we propose a simple and efficient approach for microstructure defect detection of Ti-6Al-4V titanium alloy based on image analysis. The proposed approach mimics the way that domain experts identify the defect area, by segmenting material grains via image preprocessing and detecting defects using region-based graph. The preprocessing step is a sequence of image processing techniques to produce potential defect regions. Next, a graph is constructed by considering the regions as nodes with connectivity determined by the pairwise distances. The connected components of this graph are the final detection result. An experiment involving 103 training and 517 testing microstructure images is carried out. The proposed method outperforms three benchmark methods with 0.919 G-mean score for the classification task. As to the performance of defect localization, the proposed approach largely outperforms two benchmark methods. In addition, the proposed method effectively detects the defect regions for 91 out of 96 defect images. Moreover, the implementation results also show that the proposed method has low computational cost. The processing time is on average 2.02 s per image, and 67.7 s for 517 images using parallel computation on a 32-core workstation.

Original languageEnglish
Article number7857097
Pages (from-to)87-96
Number of pages10
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number2
Publication statusPublished - Apr 2017
Externally publishedYes


  • connected components
  • defect detection
  • image analysis
  • region-based graph
  • titanium alloys

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computational Mathematics
  • Control and Optimization


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