Lamb waves have shown great potentials in damage detection of thin-walled structures due to their long propagation capability and sensitivity to a variety of damage types. However, their practical adoption has been hindered due to the complexity caused by their multimodal nature. Various wave modes that propagate at various velocities in the structure make the interpretation of Lamb wave signals very difficult. It is desired that the modes can be separated for independent analysis or further employment. In this article, we present our studies on the multimodal Lamb wave propagation and wave mode decomposition using frequency–wavenumber analysis. Wave representation in the frequency–wavenumber domain is obtained using multidimensional Fourier transform, where various Lamb wave modes can be easily discerned. This allows for separating them or extracting a desired wave mode through a filtering process, thus making it possible to use a single-mode Lamb wave for the detection of a certain type of damage in structural health monitoring applications. To retain the temporal and spatial information that is lost during Fourier transformation, a novel wavenumber analysis is also presented. These concepts are illustrated through experimental testing where high spatial resolution wavefields are measured by a scanning laser Doppler vibrometer.

Laminated composites are susceptible to delamination due to their weak transverse tensile and interlaminar shear strengths as compared to their in-plane properties. Delamination damage can occur internally, where it is not visible to the naked eye. Development of reliable, quantitative techniques for detecting delamination damage in laminated composite components will be imperative for safe and functional optimally designed next-generation composite structures. In this article, we study the potential of using Lamb waves for delamination detection and quantification, using model-assisted data acquisition. Novel wavenumber analysis approaches are developed and discussed to show how they can be used to investigate Lamb wave interactions with delaminated plies. Ultrasonic wave simulations are implemented to provide both in-plane and out-of-plane wave motion for the wavenumber studies. The out-of-plane results are verified against data obtained from experimental tests. It is found that the wavenumber methods can not only determine the delaminated region of the plate and its length, but can also identify the plies between which the delamination occurs. We envision that the wavenumber approaches can lead to a complete delamination quantification in the future.

Ultrasonic guided waves have proven to be an effective and efficient method for damage detection and quantification in various plate-like structures. In honeycomb sandwich structures, wave propagation and interaction with typical defects such as hidden debonding damage are complicated; hence, the detection of defects using guided waves remains a challenging problem. The work presented in this article investigates the interaction of low-frequency guided waves with core–skin debonding damage in aluminum core honeycomb sandwich structures using finite element simulations. Due to debonding damage, the waves propagating in the debonded skin panel change to fundamental antisymmetric Lamb waves with different wavenumber values. Exploiting this mechanism, experimental inspection using a non-contact laser Doppler vibrometer was performed to acquire wavefield data from pristine and debonded structures. The data were then processed and analyzed with two wavefield data–based imaging approaches, the filter reconstruction imaging and the spatial wavenumber imaging. Both approaches can clearly indicate the presence, location, and size of the debonding in the structures, thus proving to be effective methods for debonding detection and quantification for honeycomb sandwich structures.

Piezoelectric transducers are convenient enablers for generating and receiving Lamb waves for damage detection. Fatigue cracks are one of the most common causes for the failure of metallic structures. Increasing emphasis on the integrity of critical structures creates an urgent need to monitor structures and to detect cracks at an early stage to prevent catastrophic failures. This paper presents a two-dimensional (2D) cross-correlation imaging technique that can not only detect a fatigue crack but can also precisely image the fatigue cracks in metallic structures. The imaging method was based on the cross-correlation algorithm that uses incident waves and the crack-scattered waves of all directions to generate the crack image. Fatigue testing for crack generation was then conducted in both an aluminum plate and a stainless-steel plate. Piezoelectric wafer transducer was used to actuate the interrogating Lamb wave. To obtain the scattered waves as well as the incident waves, a scanning laser Doppler vibrometer was adopted for acquiring time-space multidimensional wavefield, followed with frequency-wavenumber processing. The proof-of-concept study was conducted in an aluminum plate with a hairline fatigue crack. A frequency-wavenumber filtering method was used to obtain the incident wave and the scattered wave wavefields for the cross-correlation imaging. After this, the imaging method was applied to evaluate cracks on a stainless-steel plate generated during fatigue loading tests. The presented imaging method showed successful inspection and quantification results of the crack and its growth.

A time–frequency analysis-based signal processing study for detecting active corrosion in aluminum plate-like structure utilizing the broadband piezoelectric wafer active sensors is presented in this article. Tests were conducted on an aluminum plate with a network of sensors installed on one side of the plate for Lamb wave generation and reception. The corrosion was emulated as material loss of an area of 50 × 38 mm² on the opposite side of the plate. The corroded area resulted in a thickness loss on the plate and a change in wave propagation as well. The experimental data were first evaluated by a statistical damage index (DI) based on root mean square values and then the Cohen’s class motivated cross-time–frequency analysis. The cross-time–frequency analysis was found more reliable and precise for detecting the corrosion progression when compared to the DI method. Not only can the proposed metric correctly evaluate the phase difference of specific frequency and time, it also carries useful information of phase difference, which is strongly correlated to the physics of corrosion detection using Lamb waves. Novel aspects of this study include a sensing approach that can sense corrosion damage on both external and internal surfaces of a given structure, the employment of effective tuning in corrosion detection, and using cross-time–frequency analysis to quantitatively evaluate thickness loss. Though the corrosion studied herein is an idealized and simplified situation, the subject work on phase difference and cross-time–frequency analysis is useful first-step effort and opens a new way to perform Lamb wave-based corrosion detection. The results presented in this article combine easy-to-examine corrosion assumptions together with low-frequency antisymmetric Lamb wave analysis to provide a stepping stone for more complicated analysis needed for further real life corrosion assessment.