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Abstract <p>Carbon fiber reinforced polymer (CFRP) composites are widely used in high-end manufacturing sectors such as aerospace and rail transportation due to their excellent specific strength and lightweight properties. However, delamination defects frequently occur within CFRP materials as a&#xa0;result of manufacturing processes or service conditions, posing a significant threat to structural integrity. Therefore, reliable nondestructive testing (NDT) of internal defects is of critical importance. In this study, an infrared thermography technique based on 13-bit Barker code pulse modulation is employed to enhance the signal-to-noise ratio (SNR) and detectability of defects. The acquired infrared image sequences are processed using fast fourier transform (FFT), principal component analysis (PCA), fast independent component analysis (Fast-ICA) and cross-correlation (CC) algorithms. A&#xa0;comparative analysis is conducted to evaluate the performance of each method in terms of enhancing defects contrast, suppressing background noise, and identifying deep or weak defects. Experimental results demonstrate that Barker coded thermal wave imaging, in combination with image processing algorithms, significantly improves the clarity and accuracy of defects identification in CFRP materials.</p>

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Detecting and Evaluating Defects in CFRP by using Barker Code Pulsed Thermal Wave Imaging

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Rui Zhou,
  • Tao Liu,
  • Guozeng Liu,
  • Chiwu Bu,
  • Qingju Tang

摘要

Abstract

Carbon fiber reinforced polymer (CFRP) composites are widely used in high-end manufacturing sectors such as aerospace and rail transportation due to their excellent specific strength and lightweight properties. However, delamination defects frequently occur within CFRP materials as a result of manufacturing processes or service conditions, posing a significant threat to structural integrity. Therefore, reliable nondestructive testing (NDT) of internal defects is of critical importance. In this study, an infrared thermography technique based on 13-bit Barker code pulse modulation is employed to enhance the signal-to-noise ratio (SNR) and detectability of defects. The acquired infrared image sequences are processed using fast fourier transform (FFT), principal component analysis (PCA), fast independent component analysis (Fast-ICA) and cross-correlation (CC) algorithms. A comparative analysis is conducted to evaluate the performance of each method in terms of enhancing defects contrast, suppressing background noise, and identifying deep or weak defects. Experimental results demonstrate that Barker coded thermal wave imaging, in combination with image processing algorithms, significantly improves the clarity and accuracy of defects identification in CFRP materials.