Statistical Analysis of Defect Dection in Glass Fiber Reinforced Polymers Using Frequency Modulated Thermal Wave Imaging

Active infrared thermography (AIRT) has emerged as an extensively applied non-destructive testing and imaging (NDT&I) methodology for examining the characteristics of the material, without impacting their future utility. The proposed work demonstrates the evaluation of a glass fiber reinforced polymer (GFRP) composite test sample fabricated with flat bottom holes as defects at varying depths using frequency modulated thermal wave imaging (FMTWI) as an emerging nonstationary thermal wave imaging (NSTWI) modality. The properties of pulse compression favorable FMTWI is eminent for compressing the total imposed thermal energy into a narrow pulse to enhance the depth resolution as well as the test sensitivity. The reliability analysis of pulse compression favorable FMTWI method is achieved via a statistical parameter known as probability of detection (PoD). In this paper, continuous signal response model is taken into consideration by computing the area under the main central lobe of the reconstructed profiles, obtained from the process of cross-correlation at all depths of GFRP specimen. For statistical evaluation, peak signal-to-noise ratio (PSNR) and Tanimoto criterion have been used as parameters of merit. Moreover, the framework based on estimation of probability of detection considering main lobe area (MLA) for apparent visibility and resolution of defects has been proposed. The results claim that by applying MLA as a statistical feature of reliability estimation, the defects having higher aspect ratio are detected with more than 90% probability.