Abstract
Carbon fibre reinforced polymer (CFRP) composites have been extensively applied in the aerospace industry. However, during the service life, CFRP composites always suffer from delamination and other subsurface defects. In order to improve the safety performance of the CFRP composites, Barker code pulse-compression thermography (PuCT) has been proposed for detecting and identifying subsurface delamination defects. A series of 15 artificial flat-bottomed holes (FBHs) were prepared to simulate the subsurface delamination defects in CFRP specimen. A 13-bit Barker code PuCT test scheme was proposed to heat the surface of specimen, and the infrared image sequence was captured by an infrared camera A655sc. The Barker code PuCT original image sequences was processed by 3D-matched filter (3D-MF) technique, combined with various sub-filtering methods included simple matched filter (SMF), matched subspace filter (MSF), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). The signal-to-noise ratio (SNR) was calculated to evaluate the detection capabilities of different processing methods. The results show that the 3D-MF significantly enhances the SNR and reduces the background noise, and obtains better defect recognition than PCA. 3D-MF-QDA combined the advantages of Barker code PuCT, 3D-MF and supervised learning (SL) technique to accurately identify defects with a diameter-to-depth ratio of 4 or more, and the actual defect diameters identification accuracy was 97.5%, 96.7%, 86%, 86% and 71.5%, respectively. The results demonstrate the potential of Barker code PuCT with 3D-MF to effectively detect defects in CFRP composites and improve the accuracy of defect size identification.
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