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• Besides dispersion, comprehensive amplitude modulation can also change the appearance of Lamb wave signals. • A dictionary reconstruction strategy is established with considerations of phase and amplitude information. • The performance of sparse imaging method relies on the denoising parameters and numbers of sources. 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. Besides dispersion, signal changes caused by amplitude modulation should be considered for waveform distortion when constructing the dictionary for sparse imaging method. In this paper, a non-contact laser is used for Lamb wave excitation which exhibits a strong amplitude modulation in low frequency. Additionally, the strong attenuation resulting from material damping would also presents a distance-dependent amplitude modulation. To reconstruct an amplitude model of Lamb wave, the decomposition method of system response and attenuation is proposed. Then, the influence of amplitude modulation on signal representation is analyzed, which shows the restriction of dictionary without considering amplitude modulation. On this basis, the amplitude considered dictionary is built together with the phase considered dictionary for sparse imaging in terms of damage detection. Furthermore, according to Lamb wave reflection model, the solution for sparse reconstruction imaging is given. Finally, the performance of sparse imaging method is discussed by experimental investigation with different parameters. The results show the efficiency of the proposed method with improved imaging performance and give comparisons for better parameter choice. [ABSTRACT FROM AUTHOR]

The complex design of composite structures makes it very difficult for conventional visual inspection techniques to detect defects in these materials. Therefore, numerous types of nondestructive testing and structural health monitoring techniques have been developed for damage identification in composite structures, among which ultrasonic guided waves, particularly Lamb waves, have gained much popularity. In recent years, apart from piezoelectric pointwise measurements, laser Doppler vibrometry has been used for full wavefield measurements of propagating Lamb waves. Damage imaging can be done with animations of Lamb waves interacting with defects. In this work, two end-to-end deep learning-based models of many-to-one sequence prediction were developed to perform pixel-wise image segmentation. A large dataset of full wavefield images resulting from the interaction with delamination of random size, shape, and location was utilised to train the proposed deep learning models. The main goal of this work is to investigate the feasibility of implementing deep learning methods for delamination identification in composite laminates by only utilising the animations of Lamb waves. The elaborated models worked well on the numerically generated test data and have also shown good performance on the real-world experimental data. These methods can enable the automation of delamination identification and to create damage maps without the intervention of the user. [ABSTRACT FROM AUTHOR]

• The Lamb wave can be only efficient excited and received when the transmitter and receiver are arranged along with the warp or weft direction of the CFRP laminates. • The BVIDs (Barely Visible Impact Damages) in 3 J and 7 J impact areas can be intuitively located, and the BVIDs around the macro impact damage can be also clearly displayed. • The method shows high application significance in improving the capability of nondestructive testing, and can be used for ensuring the safety operation of CFRP composites. The traditional linear ultrasonic technique may not provide a clear indication of the underlying damage in some cases of invisible or barely-visible impact damage. Considering nonlinear ultrasonic methods have been used for the detection of defects such as closed crack or delamination, a new nonlinear Lamb wave imaging method for detecting the Barely Visible Impact Damage (BVID) of Carbon Fiber Reinforced Polymer (CFRP) laminates was proposed in this work. First, the testing platform of nonlinear Lamb wave was built, and the test specimen with different impact damages was prepared. Second, the Lamb wave modes that meet the requirements of nonlinear testing were excited, and then, the correlation between the relative non-linear coefficient and status of low-energy impact damage were investigated. Lastly, the scanning imaging methods were developed, and the Damage Index (DI) for imaging impact damage was defined. The research results indicated that the DI based on the relative non-linear coefficient has higher detection sensitivity for low-energy impact damage, and the BVID and the extension of macro damage can be displayed intuitively by use of the new nonlinear Lamb wave imaging method. [ABSTRACT FROM AUTHOR]

Lamb wave is a promising means for active structural health monitoring and nondestructive evaluation of laminated composite plates. The inevitable reflections from structural geometric boundaries and other interferences limits the imaging performance of many existing imaging methods. The image generated through the well-known delay-and-sum method holds a wide mainlobe width and high level of sidelobes. Although the sparse reconstruction method is free from those deficiencies, it is sensitive to the regularization parameter. To overcome those limitations, a Lamb wave imaging method to locate anomalies or damage in laminated composite plates is proposed. The scattering signals are sparsely decomposed one by one with a prior weights penalized on the undetermined sparse coefficients, and the minimum variance distortionless response algorithm is adopted to process those sparse coefficients so as to generate the image. Experimental results on a quasi-isotropic laminated composite plate demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The main purpose of the present study is to investigate the effect of adding carbon nanofibers (CNFs) in fibrous polymeric composites on the reduction of matrix cracking. A tensile testing machine was used to apply five loading steps and induce different crack densities in [0 2 /90 6 ] s composite specimens with and without CNF fillers. After each loading step, the elastic modulus of specimens was measured. The crack density of damaged specimens was also measured by an optical microscope after each loading step. The Lamb wave propagation method was used to assess the elastic modulus of specimens at different crack densities. It was demonstrated that the addition of CNF fillers into the composite specimens resulted in lower crack densities and less stiffness reduction at certain applied stress levels. Moreover, Hashin and Andersons et al. micromechanical models were assessed by the experimental results for the analysis of matrix cracking in cross-ply laminated composites. For composite specimens with and without CNFs, the stiffness reduction at different crack densities was in good agreement with the predictions of the Hashin model, and the induced crack density at each applied loading step was in good agreement with the predictions of the Andersons et al. model. • The Lamb wave propagation method offers a strong tool for nondestructive evaluation of hybrid composite materials. • The matrix crack density and the stiffness reduction decreased by adding carbon nanofiber into the matrix of the specimens. • The stiffness reduction after each loading step was in good agreement with the predictions of the Hashin model. • The induced crack density after each loading step was in good agreement with the predictions of the Andersons et al. model. • The enhancement of the strength of the 90-degree layers was the major factor in decreasing the matrix crack density. [ABSTRACT FROM AUTHOR]

Lamb wave techniques have been widely used for structural health monitoring (SHM) and nondestructive testing (NDT). To deal with dispersive and multimodal problems of Lamb wave signals, many signal processing methods have been developed. A spatially distributed array of piezoelectric transducers is generally adopted for both transmission and reception of Lamb waves. When imaging the damage in composite laminates, it is necessary to meet the need of processing array signals with high efficiency. In this paper, the multiple sparse Bayesian learning (M-SBL) strategy is employed for damage imaging. Multiple residual signals including damage-reflection waves are decomposed into a sparse matrix of location-based components simultaneously. An appropriate dictionary is designed to match the damage-reflection waves instead of interference waves. The key to success is to obtain the sparse matrix of weighting coefficients through the M-SBL algorithm. Damage imaging can be achieved efficiently using the delay-and-sum (DAS) method with sparse coefficients in time-domain. Results from the experiment in composite laminates demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The purpose of the present study is to develop a nondestructive evaluation method and propose a model-independent viscoelastic characterization approach for the identification and classification of the matrix cracking in polymeric composites using the Lamb wave propagation method. To this end, cross-ply laminated composite specimens were fabricated and matrix cracking with different crack densities was induced in specimens using a tensile testing machine. The crack density was first measured by an optical microscope and compared with that of predicted by the Hashin model. Then, the fundamental anti-symmetric Lamb wave (A 0) at a frequency of 206 kHz was propagated in specimens using an ultrasound actuator and an ultrasound probe to obtain the characteristics of the induced waves including the displacement phase and the amplitude at different locations of the specimens along the propagation direction. Then, the corresponding inverse Lamb wave propagation problem was solved and the elastic and viscoelastic properties of specimens were estimated. The elastic modulus measured by the present method was in good agreement with the results obtained by an optical microscope and predicted by the Hashin model. The viscoelastic properties of specimens also showed good sensitivity to the severity of the induced matrix cracking. [ABSTRACT FROM AUTHOR]

Stiffened composite panels are widely used in industry, and their safety requirements have drawn great attention. However, non-destructive testing and evaluation (NDT&E) methods for these panels are still limited. This is due to the fact that the stiffeners induce complex signals during damage detection. This paper presents a new damage detection method for stiffened composite panels based on Lamb waves. The method combines a quantitative wavefront expression for an anisotropic composite and the conventional time-of-flight (ToF) method to detect debonding damage between stiffeners and the composite skin as well as low-velocity impact damage in the skin sheet. Initially, Lamb wave propagation characteristics are evaluated by numerical simulations to obtain mode conversion at the stiffener and delay after transmission through the stiffener. Then the numerical simulations and experiments are implemented in stiffened cross-ply composite laminates to verify the effectiveness and accuracy of the proposed method. Results indicate that the proposed method can accurately locate damage and has potential for industrial application. [ABSTRACT FROM AUTHOR]

Lamb wave based sparse reconstruction imaging is promising for damage localization in structural health monitoring (SHM) and nondestructive testing (NDT). This method requires accurate dictionary construction to match damage reflected Lamb wave signals. Damage is assumed to be a point-like reflector in previous research, however, most structural damages tend to occupy a physical extent within the structure, which limits dictionary accuracy and thus reduce the performance of sparse reconstruction imaging. In this study, dictionary construction is extended to both point-like damage and damage occupying a physical extent, a modified sparse reconstruction imaging method is subsequently proposed based on the extended dictionary. Compared with conventional method, imaging performance of the modified method is improved due to superior dictionary construction. Another advantage of modification is the ability of forming damage outline for quantitative evaluation. The effectiveness of the modified method is experimentally demonstrated with simulated delamination in composite laminates. • Lamb wave sparse reconstruction is a strategy for damage imaging in SHM and NDT. • Modified sparse method based on superior dictionary construction is proposed. • The modification can improve imaging performance of damage in CFRP. [ABSTRACT FROM AUTHOR]