Periodic permanent magnet(PPM) electromagnetic acoustic transducers (EMATs) are commonly employed for axial defect inspection in pipelines. However, the lowest-order shear horizontal waves (SH0) guided waves have difficulties in distinctly differentiating internal and external defects. To enhance the signal-to-noise ratio and resolution, a unidirectional electromagnetic acoustic transducer (EMAT) based on Circumferential Lamb waves (CLamb waves) is developed. Through structural parameter optimization and excitation frequency adjustment, high-amplitude and low-dispersion CLamb waves are successfully generated in the high-frequency-thickness product region of the dispersion curve. Finite element simulations and experimental validation confirm the capability of this EMAT in exciting CLamb waves for the detection of crack-like defects. Experimental results demonstrate that the excitation efficiency of the CLamb EMAT exceeds that of the periodic permanent magnet electromagnetic acoustic transducer by more than tenfold. The defect reflection signal of the CLamb EMAT exhibits higher resolution and more significant amplitude compared to the PPM EMAT. The integration of this method with SH0 mode detection allows for the inspection of both internal and external defects in pipelines, offering a new avenue for EMAT applications in pipeline inspection.

An electromagnetic acoustic transducer (EMAT) is suitable for measuring the propagation time more accurately without causing abrasion to the transducer during testing due to the principle of its excitation. This work designs a flux-concentrating EMAT with a radial-flux-focusing permanent magnet to significantly enhance static magnetic field strength. Through theoretical analysis and finite element simulation, two kinds of coils are designed according to the concentration areas of the horizontal and vertical components of the magnetic field. One is used to generate pure longitudinal waves, and the other is used to generate both longitudinal waves and shear waves. The experimental comparison shows that the amplitudes of the pure longitudinal wave and the dual-mode wave excited by the two kinds of coils with the radial-flux-focusing magnet are more than two times higher than those with the ordinary magnet. Therefore, the flux-concentrating EMAT with the appropriate coil provides an insight into realizing more accurate detection where longitudinal wave detection is required.

Ultrasonic focusing transducers have broad prospects in advanced ultrasonic non-destructive testing fields. However, conventional focusing methods that use acoustic concave lenses can disrupt the acoustic impedance matching condition, thereby adversely affecting the sensitivity of the transducers. In this paper, an active focusing planar ultrasonic transducer is designed and presented to achieve a focusing effect with a higher sensitivity. An electrode pattern consisting of multiple concentric rings is designed, which is inspired by the structure of Fresnel Zone Plates (FZP). The structural parameters are optimized using finite element simulation methods. A prototype of the transducer is manufactured with electrode patterns made of conductive silver paste using silk screen-printing technology. Conventional focusing transducers using an acoustic lens and an FZP baffle are also manufactured, and their focusing performances are comparatively tested. The experimental results show that our novel transducer has a focal length of 16 mm and a center frequency of 1.16 MHz, and that the sensitivity is improved by 23.3% compared with the conventional focusing transducers. This research provides a new approach for the design of focusing transducers.

Deep learning has significantly advanced the field of pipeline circumferential guided wave testing. However, the scarcity of defective samples in non-destructive testing datasets poses a significant challenge due to data imbalance. Traditional computer vision augmentation techniques, such as rotation and flipping, are often not directly applicable to guided wave data due to its unique characteristics. To address this issue, we propose a novel approach that combines the strengths of convolutional neural networks (CNNs) and random forest (RF) classifiers. Raw detection data are preprocessed using continuous wavelet transform to generate informative time-frequency images. Random forests enhance predictive accuracy by combining multiple decision trees. The experiment results demonstrate the effectiveness of the proposed CNN-RF approach, particularly in handling imbalanced datasets. When the data imbalance ratio is more than 1/2, the shallow, lightweight CNN could effectively extract signal features and lead to approximately a 56.5% speedup. When the imbalance ratio is less than 1/2, the ResNet34-RF model significantly outperforms the standard CNN model. This approach holds potential for contributing to the miniaturization, lightweight design, and mobility of guided wave detection systems and devices.

To address the challenges of automatic detection caused by the variation of surface normal vectors in automotive steering knuckles, an automatic detection method based on ultrasonic phased array technology is herein proposed. First, a point cloud model of the workpiece was constructed using ultrasonic distance measurement, and Gaussian-weighted principal component analysis was used to estimate the normal vectors of the point cloud. By utilizing the normal vectors, water layer thickness during detection, and the incident angle of the sound beam, the probe pose information corresponding to the detection point was precisely calculated, ensuring the stability of the sound beam incident angle during the detection process. At the same time, in the trajectory planning process, piecewise cubic Hermite interpolation was used to optimize the detection trajectory, ensuring continuity during probe movement. Finally, an automatic detection system was set up to test a steering knuckle specimen with surface circumferential cracks. The results show that the point cloud data of the steering knuckle specimen, obtained using phased array ultrasound, had a relative measurement error controlled within 1.4%, and the error between the calculated probe angle and the theoretical angle did not exceed 0.5°. The probe trajectory derived from these data effectively improved the B-scan image quality during the automatic detection of the steering knuckle and increased the defect signal amplitude by 5.6 dB, demonstrating the effectiveness of this method in the automatic detection of automotive steering knuckles.