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Abstract <p>Honeycomb sandwich structures are widely used in aerospace and other engineering fields due to their high strength-to-weight ratio and excellent mechanical properties. However, debonding defects between the skin and core layers often occur during service, which makes reliable nondestructive testing essential. In this study, a square-wave infrared thermography method is investigated for the detection of debonding defects in CFRP/Al honeycomb panels. Both simulation and experimental studies are conducted. A multi-algorithm fusion method combining pulse phase thermography (PPT), principal component analysis (PCA), and total harmonic distortion (THD) is proposed to enhance the defect visibility in thermal image sequences. Each algorithm extracts complementary features – phase contrast, principal component energy concentration, and nonlinear distortion response, respectively. These features are normalized and fused to generate an enhanced defect image. Simulation is conducted using COMSOL to model the temperature response of the honeycomb structure. Experimental results show that the proposed PPT-PCA-THD fusion enhancement (PPTFET) method improves the signal-to-noise ratio (SNR) of the defect images, outperforming single-method approaches demonstrating its potential for accurate nondestructive evaluation of honeycomb structures.</p>

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Multi-Algorithm Fusion Infrared Thermography for Debonding Defect Detection in CFRP/Al Honeycomb Structures

  • 期刊论文

  • 发表日期：

    2025年11月1日

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  • Xibin Zhang,
  • Xin Huang,
  • Yongquan Zhou,
  • Guozeng Liu,
  • Yongjun Ma,
  • Zesen Liu

摘要

Abstract

Honeycomb sandwich structures are widely used in aerospace and other engineering fields due to their high strength-to-weight ratio and excellent mechanical properties. However, debonding defects between the skin and core layers often occur during service, which makes reliable nondestructive testing essential. In this study, a square-wave infrared thermography method is investigated for the detection of debonding defects in CFRP/Al honeycomb panels. Both simulation and experimental studies are conducted. A multi-algorithm fusion method combining pulse phase thermography (PPT), principal component analysis (PCA), and total harmonic distortion (THD) is proposed to enhance the defect visibility in thermal image sequences. Each algorithm extracts complementary features – phase contrast, principal component energy concentration, and nonlinear distortion response, respectively. These features are normalized and fused to generate an enhanced defect image. Simulation is conducted using COMSOL to model the temperature response of the honeycomb structure. Experimental results show that the proposed PPT-PCA-THD fusion enhancement (PPTFET) method improves the signal-to-noise ratio (SNR) of the defect images, outperforming single-method approaches demonstrating its potential for accurate nondestructive evaluation of honeycomb structures.