Abstract
Quantitative non destructive subsurface analysis with increased reliability for defect detection makes it useful for a variety of industrial applications to assess the integrity and subsequent strength of materials either during or post manufacturing. Traditional non stationary thermal wave based subsurface analysis approaches are skill intensive and time consuming for analysis with human intervention. This paper proposes an automated classification and regression tree based quantitative post processing modality along with thermal wave model to characterize the subsurface anomalies using quadratic frequency modulated thermal wave imaging. It also validates the proposed mathematical modeling using experimentation carried over carbon fiber reinforced and glass fiber reinforced plastic specimens used in aerospace industry. Subsurface details have been visualized in terms of their depths using the proposed modality being evaluated from the proposed mathematical model. In addition, its detection capability and reliability over other contemporary approaches have been assessed using signal to noise ratio and probability of detection respectively.
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