Compared to other contemporarily used non-destructive evaluation (NDE) techniques, resonance inspection (RI), which employs the natural vibrational frequency spectra shift induced by the damage to detect defects, is advantageous in many aspects such as low cost, high testing speed, and broad applicability to complex structures. However, the inability to provide damage details, i.e. location, dimension, or types, of the flaws severely hinders its wide spread applications and further development despite its early success in the automobile industry for quality inspections of safety critical parts. In this study, an inverse RI algorithm using a maximum correlation function as the filtering function is proposed to quantify the location and size of flaws for a discrepant part. The algorithm and the numerical schemes are validated using a dog-bone shaped stainless steel sample, while the spectrum data for the original part and flawed parts were generated by a commercial FEM package. The results show that multiple flaws can be accurately identified using the proposed RI inversion method. The study further showed that the reliability of the inversion method is sensitive to the spectrum range included in the correlation function computation. It is demonstrated that the frequency range required to provide accurate predictions is inversely correlated to the defect size. Large defects can be detected using lower frequency spectrum data only, while smaller defects require a higher frequency range.

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