Laser ultrasonic backscattering is of great significance to nondestructive evaluation of the microstructure of strong-scattering metal materials. However, it is difficult to extract the microstructural backscattering signals because of high-level electrical noises. In this manuscript, the laser ultrasonic backscattering characteristics of metal microstructure are analyzed and studied based on the Empirical Mode Decomposition (EMD). TA2 titanium alloy was heat-treated at 800°C for different times to obtain single-phase titanium alloy specimens with different grain sizes. The laser-ultrasonic waveforms transmitted by the bottom surface of the sample were obtained. The EMD was performed on the backscattering noise signal between two successive pulse echo signals, and the multi-order Intrinsic Mode Function (IMF) was obtained. The correlation between the average spectrum of multiple measurements of each order IMF and the spectrum of the first longitudinal wave pulse echo was analyzed. and the IMF with the largest correlation coefficient was selected as the effective IMF, which was the most relevant to the microstructure. Subsequently, the backscattering levels of each sample were calculated based on the variance analysis of the effective IMF measured at multiple points. The results have shown that the backscattering level is positively correlated with the grain size of the metal structure in a specific frequency range.
As an important part of license plate recognition system, research for the license plate detection has made great progress in recent years, it is still affected by complex environments such as weather, distance, angle and brightness. Therefore, a MFA-UNet model is proposed in this paper, which is based on the UNet model structure and combines the multi-scale convolution feature fusion module and the spatial attention mechanism. In the last two layers of the up-sampling stage, the multi-scale dilated convolution feature fusion module is used to cancel the pooling operation, which ensures that the receptive field can be increased without losing the image resolution, and the image features can be enhanced. The attention of the license plate area is increased by introducing a spatial attention mechanism; the learning and training process have been optimized by using the focal loss function. Based the experiments results, accuracy of the model algorithm mentioned in this paper is 4.5% higher than the original UNet in IoU (Intersection over union), and average detection accuracy of the MFA-UNet model on the Chinese City Parking Dataset (CCPD) dataset is 97.8%, which is a great improvement compared with the target detection algorithm.
Laser-EMAT (electromagnetic acoustic transducer) technology has the advantages of laser induced ultrasound and
EMAT simultaneously. This paper introduced a novel simulation about laser-EMAT testing the surface crack with
various depth in the aluminum block. A finite element model of laser-EMAT detection was set up for investigating the
influence of groove-type crack on ultrasonic propagation in the way of numerical simulation. Then, the response curve of
voltage in time domain was obtained by the testing coil in EMAT, which is proposed to determine the depth of crack
above. Good coupling could be found between voltage of signal received by EMAT coil and amplitude of ultrasound
generated by the laser. In addition, the snapshot of ultrasound field at different time demonstrates mode conversion
occurs when the surface wave propagated through the crack. The simulation results show the relative error of
determining crack depth by the proposed method is less than 6.5%.
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