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Thermographic Signal Reconstruction (TSR) is a processing technique in Themography for Nondestructive Testing (TNDT). It is based on a least square fit of a low order polynomial to the logarithmic time evolution of experimental data. Even though TSR allows the reduction of data for processing and the filtering of high frequency noise, the resulting TSR polynomial coefficients lack of physical meaning to provide quantitative results and further processing is required in order to characterize internal defects. We propose to use Artificial Neural Networks (ANN) as a tool to map between TSR coefficients and defect depths. This paper presents the application of ANN and TSR coefficients as learning and validation data sets to characterize defects in composite materials.

Defect Quantification with Thermographic Signal Reconstruction and Artificial Neural Networks