Support vector machines based defect recognition in SonicIR using 2D heat diffusion features

In SonicIR, when a single short pulse of 20 or 40 kHz sound wave passes through materials with mechanical discontinuities, e.g., cracks with faying surfaces, it will ordinarily cause heating of those surfaces. This study investigates the effect of support vector machines (SVM), which is a machine-learning method based on the principle of structural risk minimization, as a classifier tool to identify defects in SonicIR image sequences. One inconel sample with a known defect has been chosen to construct the training set, and the 2D heat diffusion patterns of defect and disturbing signals at two different times during the sonic pulse have been chosen as features to be used in the classification procedure. A two stages SVM classifier has been employed to recognize defects in 80 inconel and 60 titanium samples, the results indicate that SVM is a promising tool for defect recognition in SonicIR image sequences.