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Fuzzy Decision Tree Based Characterization of Subsurface Anomalies

Recent intervention of machine learning based methodologies into infrared thermography proves to provide better defect detection and characterization. This paper provides a Fuzzy decision tree based quantitative post processing modality along with a theoretical model for thermal waves to characterize the subsurface anomalies using quadratic frequency modulated thermal wave imaging. A carbon fiber reinforced polymers (CFRP) and mild steel (MS) specimens having flat bottom holes with different depths and sizes are processed through experimental evaluation. A direct depth estimation is provided by the proposed modality being evaluated from the proposed mathematical model. In addition, its detection capability and reliability over other contemporary approaches is compared using signal to noise ratio, defect sizing and probability of defect detection.