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Density feature clustering method for detection of internal defects in infrared non-destructive testing.

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Title: Density feature clustering method for detection of internal defects in infrared non-destructive testing.
Authors: Zhang, Jin-Yuan1,2 13659859009@163.com, Zhao, Xuan1,2, Wang, Zhao-Jun1,2, Zhou, Xiao-Long1,2
Source: Insight: Non-Destructive Testing & Condition Monitoring. Jul2026, Vol. 68 Issue 7, p443-449. 7p.
Subjects: Nondestructive testing, Clustering algorithms, Image reconstruction, Thermography, Laminated materials
Abstract: Aiming at the detection of internal voids, internal expanded void defects and through-hole defects in aerospace composite materials, blind holes, conical through holes with different diameters and through holes with the same diameter were experimentally fabricated to simulate different diameters and types of defect existing in such materials. On this basis, the defects were detected using the pulsed thermal stimulation infrared non-destructive testing method. Since the original thermal wave image recorded by the infrared thermal imager cannot fully reflect the defect information, the defect recognition ability is enhanced and the contrast between the defect region and the non-defect region is improved by using the image sequence reconstruction algorithm based on feature density clustering. Finally, the proposed method was compared with thermographic signal reconstruction (TSR), principal component analysis (PCA) and pulsed phase thermography (PPT) and the superiority of detection in internal voids and internal expanded void defects was verified. [ABSTRACT FROM AUTHOR]
Copyright of Insight: Non-Destructive Testing & Condition Monitoring is the property of British Institute of Non-Destructive Testing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 195295811
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Density feature clustering method for detection of internal defects in infrared non-destructive testing.
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  Data: <searchLink fieldCode="JN" term="%22Insight%3A+Non-Destructive+Testing+%26+Condition+Monitoring%22">Insight: Non-Destructive Testing & Condition Monitoring</searchLink>. Jul2026, Vol. 68 Issue 7, p443-449. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Thermography%22">Thermography</searchLink><br /><searchLink fieldCode="DE" term="%22Laminated+materials%22">Laminated materials</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Aiming at the detection of internal voids, internal expanded void defects and through-hole defects in aerospace composite materials, blind holes, conical through holes with different diameters and through holes with the same diameter were experimentally fabricated to simulate different diameters and types of defect existing in such materials. On this basis, the defects were detected using the pulsed thermal stimulation infrared non-destructive testing method. Since the original thermal wave image recorded by the infrared thermal imager cannot fully reflect the defect information, the defect recognition ability is enhanced and the contrast between the defect region and the non-defect region is improved by using the image sequence reconstruction algorithm based on feature density clustering. Finally, the proposed method was compared with thermographic signal reconstruction (TSR), principal component analysis (PCA) and pulsed phase thermography (PPT) and the superiority of detection in internal voids and internal expanded void defects was verified. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Insight: Non-Destructive Testing & Condition Monitoring is the property of British Institute of Non-Destructive Testing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1784/insi.2026.68.7.443
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      – Code: eng
        Text: English
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        PageCount: 7
        StartPage: 443
    Subjects:
      – SubjectFull: Nondestructive testing
        Type: general
      – SubjectFull: Clustering algorithms
        Type: general
      – SubjectFull: Image reconstruction
        Type: general
      – SubjectFull: Thermography
        Type: general
      – SubjectFull: Laminated materials
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      – TitleFull: Density feature clustering method for detection of internal defects in infrared non-destructive testing.
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            NameFull: Zhang, Jin-Yuan
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            NameFull: Zhao, Xuan
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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