The Feature Recognition of CFRP Subsurface Defects Using Low-Energy Chirp-Pulsed Radar Thermography

In this article, a low-energy chirp-pulsed radar thermography (CP-RT) is used to detect the subsurface delamination of carbon fiber reinforced polymer (CFRP) composite as nondestructive testing and evaluation (NDT&E) techniques. The CFRP specimen with artificial flat-bottom holes (FBHs) is prepared for NDTandE by chirp-pulsed radar thermography. Two lasers are employed to be external excitation heat sources. The laser intensities are modulated according to a chirp-pulsed radar signal that combines linear frequency modulation and pulse excitation, and the temperature rise is controlled within 2 °C in the experiment. The thermal-wave response signal is processed by a series of different postprocessing characteristic extraction algorithms. These algorithms include time–frequency algorithms [crosscorrelation algorithm (CC), fast Fourier transform (FFT), and dual-orthogonal demodulation algorithm (DOD)] and statistical analysis approaches [principal component analysis (PCA) and PCA-based reconstructed independent component analysis (PCA-RICA)]. The signal-to-noise ratio (SNR) of defects is employed to evaluate the defect detectability for different size defects by different postprocessing algorithms. A three-dimensional (3-D) tomography method based on the FFT phase characteristic is proposed. A truncated-correlation photothermal tomography based on DOD is also introduced to enable the 3-D tomography of CFRP specimen. The FFT phase presents a relatively high SNR and has good correlation with the depth of the defect. The FFT-based CP-RT has the potential to provide a rapid NDT&E and 3-D tomography approach for CFRP with subsurface defects under the low-energy excitation condition.

• Publication year

  2020

• Venue

  IEEE Transactions on Industrial Informatics

• Publication date

  2020-08-01

• Fields of study

  Materials Science, Computer Science, Engineering

• Identifiers
  DOI 10.1109/TII.2019.2954718
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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