Abstract
ABSTRACT In ultrasonic testing of Carbon Fiber Reinforced Polymers (CFRP), signals of near-surface flaws are often submerged in interface signals, resulting in blind spots for defect detection. To address this issue, this paper presents an autocorrelation imaging algorithm that combines Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with l 0 -norm sparse representation. Firstly, the ultrasonic signal can be modelled as a convolution process. By using MOMEDA, the sequence of reflected pulses in the ultrasonic signal is obtained. Then, the l 0 -norm sparse representation is utilised to enhance the temporal resolution of the sequence, successfully separating near-surface defect signals from interface signals. Finally, the processed data is input into the autocorrelation algorithm, achieving automated imaging of near-surface flaws. Simulation results demonstrate the efficacy of the algorithm in separating overlapping signals. Finally, experimental validation was conducted on flaws at three different depths. The algorithm presented in this paper is capable of identifying all near-surface flaws, with a defect size error rate consistently below 5%.
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