4.7 Article

Multimodal optical excitation pulsed thermography: Enhanced recognize debonding defects of the solid propellant rocket motor cladding layer

期刊

出版社

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2021.108164

关键词

Feature extraction algorithm; Interface debonding defects; Solid propellant rocket motor; Multimodal optical excitation pulsed thermography

资金

  1. Foundation for Innovative Research Groups of the National Nature Science Foundation of China [51521003]
  2. Chinese National Natural Science Foundation [61571153, 51173034]
  3. China National Postdoctoral Program for Innovative Talents [BX2021092]
  4. China Postdoctoral Science Foundation [2021M690841]
  5. Self-planned Task of State Key Laboratory of Robotics and System (HIT)
  6. Programme of Introducing Talents of Discipline of Universities [B07108, 2019-JCJQ-JJ308]
  7. Heilongjiang Province Postdoctoral Science Foundation [LBH-Z20019]
  8. Aeronautical Science Foundation of China [2020Z057077001]

向作者/读者索取更多资源

The study demonstrated the effectiveness of multimodal optical excitation pulsed thermography in enhancing defect detectability and depth-resolution dynamic range for solid propellant rocket motor interfaces. By building a 3D thermal-wave model, proposing feature extraction algorithms, and optimizing the pulse thermography with PLSR and ICA, better detection of interface debonding defects was achieved. Analysis of characteristic profiles indicated that the 1st independent component was more effective in detecting defect depth and diameter.
We demonstrated the multimodal optical excitation pulsed thermography, and this technique can enhance the defect detectability and the depth-resolution dynamic range for the propellant /cladding layer interface debonding defects of the solid propellant rocket motor. Firstly, threedimensional (3D) thermal-wave model which stimulated by a pulse excitation thermal source was built. The temperature field distribution and the thermal-wave diffusion behavior were analyzed. Subsequently, multiple feature extraction algorithms were proposed and applied to extract characteristic images. The experimental set-up was developed and utilized to detect cladding layers with artificial defects. The results demonstrate that pulse thermography optimized by PLSR and ICA can achieve better detection of interface debonding defects. The characteristic profiles were analyzed to evaluate the ability of feature images to characterize the defect diameter and depth. The results depict that the 1st independent component has a better detection effect for defect depth and diameter.

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