A Thermographic System for Quantitative and Automated Subsurface Detail Visualization

Subsurface analysis with increased reliability fascinated the post processing research in infrared imaging and non-destructive testing. This paper proposes the artificial neural network (ANN)-based post processing modality to analyze subsurface anomalies in quadratic frequency modulated thermal wave imaging and validates it using the experimentation carried over a mild steel (MS) and carbon fiber reinforced polymers (CFRP) specimens. A novice friendly thermograhic system has been developed to visualize subsurface details to quantize, detect, and visualize their corresponding depths, and further, detection capability has been compared with the contemporary processing approaches by taking the defect signal-to-noise ratio and size. In this paper, the region related active contour segmentation-based detection was used to automatic defect detection for CFRP.

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