Crack-like defects form an important type of target defect in nondestructive evaluation, and accurately characterizing them remains a challenge, particularly for small cracks and inclined cracks. In this paper, scattering matrices are used for defect characterization through use of the correlation coefficient and the structural similarity (SSIM) index as similarity metrics. A set of reference cracks that have different lengths and orientation angles are compared with the test defect and the best match is determined in terms of the maximum similarity score between the scattering matrices of the test defect and reference cracks. Defect characterization using similarity metrics is invariant to scale and shift, so calibration of experimental data is not needed. Principal component analysis (PCA) is adopted to reduce the effect of measurement noise and recover the original shape of scattering matrices from noisy data. The performance of the proposed algorithm is studied in both simulation and experiments. The length and orientation angle of four different test cracks are measured at two different noise levels in the simulation case, and excellent agreement is achieved between the measurement results and the actual values. Experimentally, the lengths of five subwavelength cracks are measured to within 0.10 mm, and their orientation angles are measured to within 5°.
|Number of pages||15|
|Journal||IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control|
|Publication status||Published - 2015|