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A defect image enhancement approach for detection of defective area in CFRPs through local defect resonance

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Abstract
Nowadays composite materials such as carbon fiber reinforced polymers (CFRP)s have been widely used in industrial applications. But, they are susceptible to impact damage and subsequent fatigue cracking and delamination which in long term lead to some negative consequences such as erosion and also breaking the material. Due to the inability to visually observe such defects and also the high sensitivity of industrial components to invasive inspections, non-destructive testing (NDT) techniques are used to deal with the aforementioned problems. In this regards, an ultrasound-based NDT technique called Local defect resonance (LDR) leads to remarkable results for detecting various types of defects in CFRPs. In LDR technique, high frequency acoustical vibrations are used to get a localized resonant activation of a defective region such that these excitation frequencies lead to a significant increase of the vibration amplitude in the defective area relative to the sound area. The problem which arises is that in order to properly localize the defect, the defect resonance frequency must be known which is practically impossible. In this paper, a new defect imaging methodology is proposed, which can localize the defects with-out any prior knowledge about their location and resonance frequencies. Experiments are performed on a CFRP sample with flat bottom hole (FBH) defects and the proposed method has been quantitatively validated through the experiments by using the signal-to-noise ratio (SNR) criterion. The results show the superiority of our method over some well-known algorithms.
Keywords
vibrations, composites, LDR, NDT

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MLA
Hadi, Saman, et al. “A Defect Image Enhancement Approach for Detection of Defective Area in CFRPs through Local Defect Resonance.” 2020 International Conference on Machine Vision and Image Processing (MVIP), IEEE, 2020, doi:10.1109/MVIP49855.2020.9116901.
APA
Hadi, S., Hasanzadeh, R., & Kersemans, M. (2020). A defect image enhancement approach for detection of defective area in CFRPs through local defect resonance. In 2020 International Conference on Machine Vision and Image Processing (MVIP). Qom, Iran: IEEE. https://doi.org/10.1109/MVIP49855.2020.9116901
Chicago author-date
Hadi, Saman, Reza Hasanzadeh, and Mathias Kersemans. 2020. “A Defect Image Enhancement Approach for Detection of Defective Area in CFRPs through Local Defect Resonance.” In 2020 International Conference on Machine Vision and Image Processing (MVIP). IEEE. https://doi.org/10.1109/MVIP49855.2020.9116901.
Chicago author-date (all authors)
Hadi, Saman, Reza Hasanzadeh, and Mathias Kersemans. 2020. “A Defect Image Enhancement Approach for Detection of Defective Area in CFRPs through Local Defect Resonance.” In 2020 International Conference on Machine Vision and Image Processing (MVIP). IEEE. doi:10.1109/MVIP49855.2020.9116901.
Vancouver
1.
Hadi S, Hasanzadeh R, Kersemans M. A defect image enhancement approach for detection of defective area in CFRPs through local defect resonance. In: 2020 International Conference on Machine Vision and Image Processing (MVIP). IEEE; 2020.
IEEE
[1]
S. Hadi, R. Hasanzadeh, and M. Kersemans, “A defect image enhancement approach for detection of defective area in CFRPs through local defect resonance,” in 2020 International Conference on Machine Vision and Image Processing (MVIP), Qom, Iran, 2020.
@inproceedings{8645978,
  abstract     = {{Nowadays composite materials such as carbon fiber reinforced polymers (CFRP)s have been widely used in industrial applications. But, they are susceptible to impact damage and subsequent fatigue cracking and delamination which in long term lead to some negative consequences such as erosion and also breaking the material. Due to the inability to visually observe such defects and also the high sensitivity of industrial components to invasive inspections, non-destructive testing (NDT) techniques are used to deal with the aforementioned problems. In this regards, an ultrasound-based NDT technique called Local defect resonance (LDR) leads to remarkable results for detecting various types of defects in CFRPs. In LDR technique, high frequency acoustical vibrations are used to get a localized resonant activation of a defective region such that these excitation frequencies lead to a significant increase of the vibration amplitude in the defective area relative to the sound area. The problem which arises is that in order to properly localize the defect, the defect resonance frequency must be known which is practically impossible. In this paper, a new defect imaging methodology is proposed, which can localize the defects with-out any prior knowledge about their location and resonance frequencies. Experiments are performed on a CFRP sample with flat bottom hole (FBH) defects and the proposed method has been quantitatively validated through the experiments by using the signal-to-noise ratio (SNR) criterion. The results show the superiority of our method over some well-known algorithms.}},
  author       = {{Hadi, Saman and Hasanzadeh, Reza and Kersemans, Mathias}},
  booktitle    = {{2020 International Conference on Machine Vision and Image Processing (MVIP)}},
  isbn         = {{9781728168326}},
  issn         = {{2166-6776}},
  keywords     = {{vibrations,composites,LDR,NDT}},
  language     = {{eng}},
  location     = {{Qom, Iran}},
  pages        = {{6}},
  publisher    = {{IEEE}},
  title        = {{A defect image enhancement approach for detection of defective area in CFRPs through local defect resonance}},
  url          = {{http://dx.doi.org/10.1109/MVIP49855.2020.9116901}},
  year         = {{2020}},
}

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