High Resolution Ultrasonic Imaging in Stiffened Composite Panels by Robust Data-Driven Matched Field Processing

Abstract

Ultrasonic imaging techniques are commonly deployed using sparse transducer arrays operated under plane wave beamforming such as delay-and-sum or time migration methods. However, in complex aerospace structures such as stiffened composite panels, wave refraction and dispersion effects significantly complicate the beamforming process. To address these challenges, the complexity of the propagating waves for impact and damage imaging can be best leveraged by using data-driven Matched Field Processing (MFP) techniques. These methods are effectively used in underwater acoustics but much less employed in SHM/NDE. This work introduces both passive and active beamforming techniques within the data-driven MFP framework using a sparse array of ultrasonic piezoelectric transducers applied to a complex aerospace panel. In particular, the broadband Coherent White Noise Constraint (C- WNC) beamformer is applied to cross correlate data-driven models and the data of interest. The C-WNC algorithm is robust against model mismatch and achieves high resolution and contrast. Its efficacy is demonstrated through passive imaging of hammer impacts on a stiffened composite panel. For damage imaging, where the damage scatterings typically act as secondary sources, conventional adaptive beamformers primarily focus on the active sources, such as a transmitter element or hammer impacts. To increase the sensitivity to structural damage in the covariance matrix, the “active” inter-element transfer functions are reconstructed by estimating the normalized cross- power spectrum between two receiver elements. This reconstruction is achieved passively by observing the structural response to a series of hammer impacts. By averaging the covariance matrix using only few hammer impacts, the C-WNC algorithm is shown to successfully image simulated damage in the stiffened panel while suppressing the “louder” impacts.