Non-contact laser ultrasonic detection technology provides an innovative solution for evaluating the internal conditions of lithium-ion batteries (LIBs), offering significant advantages in gas defect assessment and structural defect identification. This study proposes a method for evaluating internal gas defects in LIBs based on a non-contact laser ultrasonic system. The system uses a pulsed laser to generate ultrasonic waves, with a full-optical probe receiving the signals, enabling high-resolution imaging of the internal features of the battery. The study analyzes key ultrasonic characteristics under different laser parameters (energy, pulse width, and focal length) and their correlation with defective regions. Through both time-domain and frequency-domain analysis of the ultrasonic features, the results demonstrate that the signal amplitude attenuation characteristics of ultrasound in media with acoustic impedance mismatches can be used for precise detection and quantitative characterization of gas defect regions within the battery. This non-contact technology offers a promising method for real-time, non-destructive monitoring of the internal condition of lithium-ion batteries, significantly enhancing battery safety and reliability.

A numerical model based on the spectral finite element method (SFEM) is proposed to predict the electromechanical impedance (EMI) spectra of the PZT transducer bonded beam. Fast and accurate simulation of the impedance signals from the PZT wafer can be achieved by SFEM. The proposed model includes the PZT wafer (actuator/sensor), beam structure and the coupled interaction between PZT wafer/beam structure, which predicts the frequency response function, the impedance response under damage and temperature variation effects. Numerical simulation algorithms are validated with both finite element analysis and experiments. The results show that the proposed model has capability of capturing the effect of damage and temperature effects on PZT transducer impedance with great accuracy, the existence of damage can be detected by evaluating the electromechanical impedance signals.

The ultrasonic guided wave-based damage imaging methods are often limited in detection accuracy and resolution due to the dispersive characteristics of guided waves. Finding ways to extract more information from the guided wave field to improve imaging resolution has always been a hot topic in ultrasonic imaging. Based on the same set of guided wave field data obtained by numerical simulation and experiments, this paper compares the detection accuracy and resolution of the time-domain delay-and-sum (DAS) method, the frequency-domain DAS method, and the DORT-MUSIC method, which integrates time-reversal operator decomposition with multiple signal classification. The results show that, compared to the traditional time-domain imaging method, the frequency-domain method that incorporates dispersion relations exhibits significantly higher imaging accuracy. Additionally, the DORT-MUSIC method demonstrates a remarkable advantage in resolution, which can approach the diffraction limit. Related work in this paper provides a research basis for improving the imaging accuracy and resolution for ultrasonic guided waves in the practical application of structure damage detection.

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).

Capacitive micromechanical ultrasonic transducers (CMUTs), as microelectromechanical systems (MEMS) devices, have broad application prospects in ultrasonic imaging and sensing. This study investigates the influence of surface acoustic waves (SAWs) using periodically arranged CMUTs as the fundamental unit cells. We first utilize finite element analysis (FEA) to calculate and analyze the band structure and bandgap characteristics of phononic crystals under infinite periodic conditions. Subsequently, for finite periodic structures in practical applications, acoustic transmission spectra were further simulated using FEA to verify the bandgap characteristics of the structure for SAWs. Accordingly, this paper leverages a deep learning framework based on a multilayer perceptron (MLP) architecture to achieve the inverse design and optimization of CMUT geometric parameters, tailored to specific target bandgap requirements. The results demonstrate that this approach can efficiently and accurately determine the optimal structural configurations, offering a robust and novel technical paradigm for the precise control of SAWs using CMUT-based periodic arrays.