Abstract
Infrared thermographic techniques show their potential usage for non-destructive testing and evaluation of various materials due to their inherent capabilities such as safe, full field, remote, qualitative and quantitative defect detection capabilities. In this paper, a Gaussian Weighted Frequency Modulated Thermal Wave Imaging approach is reported for detection of sub-surface defects in Carbon Fiber Reinforced Polymer (CFRP) sample for a given frequency modulated incident heat flux. Artificial flat bottom holes and metallic inclusions as subsurface defects are prepared for the experimental investigation. Matched filter algorithm is applied for detection of sub surface defects by correlation coefficient images and compared the detection capabilities with conventional frequency domain phase images. The effect of spectral reshaping on frequency modulated thermal wave imaging is investigated. The results of the experiments show spectral reshaping is the most suitable selection for enhancing inspection capability and obtaining the highest Signal to Noise Ratio (SNR) for a given CFRP material.
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