Delamination is one of the common damages affecting the safety of composite structures. In this paper, a Lamb wavefield-based monogenic signal processing algorithm is proposed to quantify the delamination parameters in composite laminates, including location, size, shape, and depth. A quality-guided fast phase unwrapping algorithm is developed to solve the problem of phase wrapping after Riesz transform-based monogenic signal processing. Then, space distribution of the phase of Lamb wavefield can be extracted for calculating wavenumber distribution, which is related to the structural thickness or delamination depth and can be used for delamination imaging. Simulated Lamb wavefield signals calculated by finite element simulation are employed to evaluate the parameters of delamination in composite laminates. Compared with other traditional methods, the damage identification algorithm based on Riesz transform has excellent identification effect and shorter calculation time. The results show that the algorithm can be used not only for single delamination recognition but also for multi-delamination recognition with good accuracy. In particular, the interaction between incident waves along different ply directions and delamination is explored, and its influence on delamination quantification is studied, whose results are worthy of attention in engineering application. Finally, a completely non-contact laser ultrasonic system is established to obtain the Lamb wavefield with delamination. Experiments show that the algorithm can accurately quantify the location, size, shape, and depth of delamination.

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Lamb wave-based damage quantification in large-scale composites has always been one of the concerning and intractable problems in aircraft structural health monitoring. In recent years, machine learning (ML) algorithms have been utilized to deeply explore the damage feature of Lamb wave signals, which aims to enhance the precision and accuracy of damage quantification. However, multi-damage quantification becomes one of the bottleneck problems because ML algorithms critically depend on the dataset. In this paper, a prioritizing selection and orderly permutation method is proposed to construct multi-damage dataset based on Born approximation principle, which shows the interaction between wave signals under multi- and single-damage conditions. Based on the multi-damage dataset, a multi-task deep learning algorithm is introduced to identify multiple damage, including the damage number, location, and size, in composite laminates. In the algorithm, a multi-branch 1D-convolution neural network framework, which includes a trunk network and branch networks is established to explore the damage features in Lamb wave scattering signals. Compared with single-task models, it has the ability to learn shared features for multiple tasks, effectively boosting the task results. The results show that the proposed multi-task learning (MTL) method saves 23.03% training time compared with the single-task learning method. In the task of quantifying multiple damage of composite laminate, the results of MTL are good for both the constructed test set and the measured test set, especially in the quantification of damage size, which shows the feasibility and reliability of this method.