Abstract
In this paper, the detection capability of laser-generated bidirectional thermal wave radar imaging (BTWRI) inspection for carbon/glass fiber reinforced polymer (C/GFRP) defects was quantitatively evaluated by signal-to-noise ratio (SNR) analysis of characteristic images. A low-power experimental system was devised, and the C/GFRP specimens with multi-scale diameter-to-depth ratio were tested. An improved imaging algorithm (analytical chirp correlation, ACC) was presented by applying analytical bidirectional chirp signal to extract the correlation phase and amplitude of thermal wave radar signal corresponding to the expected delay time. The optimal ACC detection image is obtained by the SNR comparison of characteristic images frame by frame. The conventional imaging inspection results of using cross-correlation (CC), cross-correlation phase (CCP), chirp lock-in (CLI), and Hilbert transform mean (HTM) algorithms were performed for SNR comparison. Experimental results indicate that the ACC algorithm displays almost the same detection capability as the traditional CCP algorithm by a simpler calculation process, and the optimal ACC detection image has a significant enhancement on SNR performance for C/GFRP subsurface defects compared with conventional imaging algorithms.
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