Feature Recognition in Quadratic Frequency Modulated Thermal Wave Imaging for Subsurface Defect Detection in Fiber-Reinforced Polymers

Efficient processing and stimulation mechanisms facilitating subsurface feature analysis are of prime concern in composite inspection. Being capable of presenting depth resolution and depth scanning with frequency sweep at low powers makes quadratic chirp an attractive stimulation mechanism and chirp Z-phased post-processing mechanism. This paper explores this mechanism with existing contemporary approaches and presents its novel feature exhibition enhancement capability through an inspection carried over a carbon fiber reinforced polymer (CFRP) composite specimen with embedded flat bottom holes. The defect detection performance is evaluated using the defect signal-to-noise ratio (SNR) for all the feature extraction algorithms. The SNR, characteristic parameter versus defect size and depth parameters reveal that the time domain PC and frequency domain CZT phase exhibit significantly high SNR and good correlation with the defect depth.