Novel Statistical Analysis Schemes for Thermal Non-Destructive Testing and Evaluation of Hardened Steel Materials Used in Ship Building

In the field of Thermal Non-Destructive Testing and Evaluation (TNDT&E), active thermography gained popularity due to its fast, wide area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression favourable frequency-modulated thermal wave imaging stands out for its enhanced detectability and depth resolution. In this study, experimental investigation has been carried out on a hardened steel sample used in ship building industry with a flat-bottom hole simulated defect using the Frequency Modulated Thermal Wave Imaging (FMTWI) technique. And the defect detection capabilities of FMTWI have been investigated from various statistical post processing approaches and compared by taking Signal to Noise Ration (SNR) as a figure of merit. Among various adopted statistical post-processing techniques, the pulse compression has been carried out using different methods: offset removal with polynomial curve fitting, and Principal Component Analysis (PCA) which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. The performance of these techniques was assessed through experimental investigations on hardened steel specimen used in ship building to provide valuable insights into their effectiveness in defect detection capabilities.