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.
