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Automated ultrasonic testing for near-surface flaws in CFRP.

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Title: Automated ultrasonic testing for near-surface flaws in CFRP.
Authors: Wang, Tao1 (AUTHOR), Deng, Wanxin2 (AUTHOR), Wang, Haijun3 (AUTHOR), Yu, Cijun1 (AUTHOR) 22225002@zju.edu.cn
Source: Nondestructive Testing & Evaluation. Mar2025, Vol. 40 Issue 3, p968-987. 20p.
Subjects: Carbon fiber testing, Ultrasonic testing, Error rates, Entropy, Ultrasonics
Abstract: In ultrasonic testing of Carbon Fiber Reinforced Polymers (CFRP), signals of near-surface flaws are often submerged in interface signals, resulting in blind spots for defect detection. To address this issue, this paper presents an autocorrelation imaging algorithm that combines Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with ${l_0}$ l 0 -norm sparse representation. Firstly, the ultrasonic signal can be modelled as a convolution process. By using MOMEDA, the sequence of reflected pulses in the ultrasonic signal is obtained. Then, the ${l_0}$ l 0 -norm sparse representation is utilised to enhance the temporal resolution of the sequence, successfully separating near-surface defect signals from interface signals. Finally, the processed data is input into the autocorrelation algorithm, achieving automated imaging of near-surface flaws. Simulation results demonstrate the efficacy of the algorithm in separating overlapping signals. Finally, experimental validation was conducted on flaws at three different depths. The algorithm presented in this paper is capable of identifying all near-surface flaws, with a defect size error rate consistently below 5%. [ABSTRACT FROM AUTHOR]
Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd 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.)
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  Data: Automated ultrasonic testing for near-surface flaws in CFRP.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Tao%22">Wang, Tao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deng%2C+Wanxin%22">Deng, Wanxin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Haijun%22">Wang, Haijun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Cijun%22">Yu, Cijun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 22225002@zju.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Nondestructive+Testing+%26+Evaluation%22">Nondestructive Testing & Evaluation</searchLink>. Mar2025, Vol. 40 Issue 3, p968-987. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Carbon+fiber+testing%22">Carbon fiber testing</searchLink><br /><searchLink fieldCode="DE" term="%22Ultrasonic+testing%22">Ultrasonic testing</searchLink><br /><searchLink fieldCode="DE" term="%22Error+rates%22">Error rates</searchLink><br /><searchLink fieldCode="DE" term="%22Entropy%22">Entropy</searchLink><br /><searchLink fieldCode="DE" term="%22Ultrasonics%22">Ultrasonics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In ultrasonic testing of Carbon Fiber Reinforced Polymers (CFRP), signals of near-surface flaws are often submerged in interface signals, resulting in blind spots for defect detection. To address this issue, this paper presents an autocorrelation imaging algorithm that combines Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with ${l_0}$ l 0 -norm sparse representation. Firstly, the ultrasonic signal can be modelled as a convolution process. By using MOMEDA, the sequence of reflected pulses in the ultrasonic signal is obtained. Then, the ${l_0}$ l 0 -norm sparse representation is utilised to enhance the temporal resolution of the sequence, successfully separating near-surface defect signals from interface signals. Finally, the processed data is input into the autocorrelation algorithm, achieving automated imaging of near-surface flaws. Simulation results demonstrate the efficacy of the algorithm in separating overlapping signals. Finally, experimental validation was conducted on flaws at three different depths. The algorithm presented in this paper is capable of identifying all near-surface flaws, with a defect size error rate consistently below 5%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd 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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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/10589759.2024.2337062
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 20
        StartPage: 968
    Subjects:
      – SubjectFull: Carbon fiber testing
        Type: general
      – SubjectFull: Ultrasonic testing
        Type: general
      – SubjectFull: Error rates
        Type: general
      – SubjectFull: Entropy
        Type: general
      – SubjectFull: Ultrasonics
        Type: general
    Titles:
      – TitleFull: Automated ultrasonic testing for near-surface flaws in CFRP.
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            NameFull: Wang, Tao
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            NameFull: Deng, Wanxin
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            NameFull: Wang, Haijun
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            NameFull: Yu, Cijun
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          Dates:
            – D: 01
              M: 03
              Text: Mar2025
              Type: published
              Y: 2025
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              Value: 40
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            – TitleFull: Nondestructive Testing & Evaluation
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