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

• Published in: Mechanical systems and signal processing 2022-01, Vol.163, p.108164, Article 108164
• Main Authors: Wang, Fei, Liu, Junyan, Song, Peng, Gong, Jinlong, Peng, Wei, Liu, Guobin, Chen, Mingjun, Wang, Yang
• Format: Article
• Language: English
• Subjects: Algorithms; Cladding; Debonding; Defects; Diameters; Excitation; Feature extraction; Feature extraction algorithm; Interface debonding defects; Multimodal optical excitation pulsed thermography; Rocket engines; Solid propellant rocket motor; Solid propellants; Temperature distribution; Thermal wave model; Thermography; Three dimensional models
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2021.108164

[Display omitted] •The multimodal optical excitation pulsed thermography can enhance the depth-resolution dynamic range for debonding defects of the solid propellant rocket motor.•Multiple feature extraction algorithms were proposed and applied to extract characteristic parameters.•Pulse thermography optimized by PLSR and ICA can achieve better detection of interface debonding defects. 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, three-dimensional (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.