Automated extraction of local defect resonance using the principal component analysis in lock-in ultrasonic vibrothermography

Ultrasonic vibrothermography is an emerging and promising nondestructive evaluation technique used for detection of surface and sub-surface defects. The heat-generating sources such as friction of the surface asperities of the defect and viscoelastic behavior of the structure may cause variations in non-linear elastic energy leading to the rise of temperature of the damaged area. In this paper, a Flat-Bottomed Hole (FBH) defect is modeled by finite element method in a polymethylmethacrylate (PMMA) structure. The desired information from this defect is retrieved by its local defect resonance (LDR) frequency which is estimated through a Principal Component Analysis (PCA). It is shown that the PCA algorithm can extract the LDR frequency of the FBH with high accuracy. The sample is then excited by a sine wave at its LDR frequency modulated by a low frequency corresponding to the thermal penetration depth. The lock-in amplitude and phase images are also generated at different modulation frequencies in order to find the optimal frequency in terms of contrast enhancement. The results of the finite element model are then verified by comparison with published experimental results and are found to be in very good agreement.