Lamb wave-based damage assessment for composite laminates using a deep learning approach.

With the increasing utilization of composite materials due to their superior properties, the need for efficient structural health monitoring techniques rises rapidly to ensure the integrity and reliability of composite structures. Deep learning approaches have great potential applications for Lamb wave-based damage detection. However, it remains challenging to quantitatively detect and characterize damage such as delamination in multi-layered structures.

Iterative Pulse-Echo Tomography for Ultrasonic Image Correction.

Acoustic aberration, caused by the uneven distribution of tissue speed-of-sound (SoS), significantly reduces the quality of ultrasound imaging. An important approach to mitigate this issue is imaging correction based on local SoS estimation. Computed ultrasound tomography in echo mode (CUTE) is an SoS estimation method that utilizes phase-shift information from ultrasound pulse-echo signals, offering both practical utility and computational efficiency.

Energy focusing of broadband Lamb wave by designing excitation waveforms and elastic metamaterials.

In the field of structure health monitoring (SHM), the use of Lamb wave to locate damage is a common method. Energy focusing is beneficial for damage localization because of higher SNR and higher resolution. Optimization design of elastic metamaterials is promising for energy focusing based on speed modulation.

Quantitative evaluation of crack based on the sparse decomposition of array Lamb wave propagation.

Crack damage is one of the significant factors that may accumulate at the stress concentration area of engineering structures and cause catastrophic accidents. In this paper, we proposed a novel approach to identify the crack location and size by exploiting the reflections and diffractions of Lamb waves. The interaction mechanism between the crack and Lamb wave has been analyzed thoroughly, our analysis of the interaction between the crack and Lamb wave revealed that both reflections and diffractions carry valuable damage information that reflects the size and orientation of the crack.

Damage characterization using CNN and SAE of broadband Lamb waves.

The method based on Lamb wave shows great potential for structural health monitoring (SHM) and nondestructive testing (NDT). Deep learning algorithms including convolutional neural networks (CNN) and stack autoencoder (SAE) are promising to extract features from Lamb wave signals that can be linked with damage for subsequent localization and quantification. Generally, narrowband Lamb wave with purified mode and suppressed dispersion is used because of clear relationship model between damage features and recorded signals.

Improved spectrum method for impact damage characterization in the composite beam using Lamb waves.

Lamb wave spectral methods are one candidate to characterize invisible damages in composite structures. Unfortunately, multiple reflections resulting from geometric boundaries could distort the Lamb wave spectrum, which may cover the important signatures concerning structural integrity. To eliminate spectral interference, a cepstrum based filtering method is proposed to separate various reflection features.

Local Wavenumber Method for Delamination Characterization in Composites With Sparse Representation of Lamb Waves.

This article presents a quantitative Lamb wave detection method for delamination characterization in composite laminates using local wavenumber features. In contrast to the conventional Fourier transform-based methods, the improved sparse reconstruction method is efficient and able to evaluate the spatial wavenumbers of Lamb waves with limited measurements. To improve the feasibility of the sparse reconstruction method, the analytical solution of the local wavenumbers to the compressed sensing (CS) formulation with considering structural discontinuity is firstly investigated.

Amplitude modified sparse imaging for damage detection in quasi-isotropic composite laminates using non-contact laser induced Lamb waves.

Composite structure is increasingly used in civil and aerospace applications due to its high mechanical performance. Lamb wave based sparse reconstruction imaging for damage localization is promising for structural health monitoring (SHM) and nondestructive evaluation (NDE) by using few measurements. However, this dictionary based method requires accurate atoms to represent Lamb wave propagating features in structure very well.

Minimum variance imaging based on correlation analysis of Lamb wave signals.

In Lamb wave imaging, MVDR (minimum variance distortionless response) is a promising approach for the detection and monitoring of large areas with sparse transducer network. Previous studies in MVDR use signal amplitude as the input damage feature, and the imaging performance is closely related to the evaluation accuracy of the scattering characteristic. However, scattering characteristic is highly dependent on damage parameters (e.

Excitation Waveform Design for Lamb Wave Pulse Compression.

Most ultrasonic guided wave methods focus on tone burst excitation to reduce the effect of dispersion so as to facilitate signal interpretation. However, the resolution of the output cannot attain a very high value because time duration of the excitation waveform cannot be very small. To overcome this limitation, a pulse compression technique is introduced to Lamb wave propagation to achieve a δ-like correlation so as to obtain a high resolution for inspection.