The damage imaging method based on Lamb wave beamforming has been widely used in the field of SHM. The DAS method has a high imaging efficiency, but its ability to suppress interference signals is weak, resulting in low imaging resolution and signal-to-noise ratio. Drawing inspiration from the adaptive weighted MVDR damage imaging method, this paper constructs a neural network based on FCNN, with the images generated by the MVDR method as the target. By training the model, the mapping relationship between delayed channel input data and adaptive weighting factors is established, thereby improving the resolution and signal-to-noise ratio of Lamb wave damage imaging and achieving rapid damage imaging. To verify the effectiveness and imaging performance of the FCNN method, imaging of two types of damage in aluminum plates is conducted through simulation and experiments, and the imaging results are compared and analyzed with DAS and MVDR. The results show that the imaging quality and quantitative indicators of FCNN have not yet reached the performance of MVDR, but compared with DAS, FCNN has a significantly narrower main lobe width and lower sidelobe level. Furthermore, its quantitative indicators such as API, SNR, and FWHM are better than DAS. The proposed adaptive Lamb wave beamforming method based on FCNN combines high resolution and signal-to-noise ratio, as well as the advantage of rapid imaging, providing reference and support for real-time SHM based on Lamb waves.

Among damage imaging methods based on Lamb waves, the Minimum Variance Distortionless Response (MVDR) method adaptively calculates channel weights to suppress interference signals, improving imaging resolution and the signal-to-noise ratio (SNR). However, the MVDR method involves matrix inversion, which introduces a high computational burden to the implementation process and makes real-time damage detection challenging. We propose constructing a Convolutional-Neural-Network (CNN)-based network architecture based on the Delay-and-Sum (DAS) beamforming method. This architecture replaces the MVDR’s adaptive weight calculation by establishing a nonlinear mapping from multi-channel data to weighting factors, enabling efficient high-resolution Lamb wave damage imaging with an enhanced SNR. To verify the effectiveness and imaging performance of the CNN-based method, damage in an aluminum plate is imaged using both simulation and experimental methods. The imaging results are compared and analyzed against those of the DAS and MVDR methods. The results show that the proposed CNN-based adaptive Lamb wave beamforming method, which combines the advantages of a high resolution and signal-to-noise ratio, as well as rapid imaging, can provide reference and support for real-time Lamb-wave-based Structural Health Monitoring (SHM).

Acoustic imaging, especially ultrasound, underpins a wide range of applications from non-destructive evaluation to medical and materials analysis, yet its performance is ultimately constrained by lateral resolution. This review systematically summarizes recent advances in overcoming diffraction-limited resolution, encompassing traditional focusing techniques, transducer optimization, physical metamaterial lenses, and methods based on algorithmic optimization and deep learning technologies. It comprehensively covers approaches for enhancing acoustic lateral resolution, compares the differences and respective advantages and disadvantages of various methods, and proposes clear directions and recommendations for future research. This work provides robust guidance for subsequent research trends and development opportunities in higher-resolution acoustic imaging.