Wavefield imaging of nonlinear ultrasonic Lamb waves for visualizing fatigue micro-cracks.

The traditional nonlinear ultrasonic technique, as typified by the second-harmonic generation and the frequency mixing response, can be employed to identify and characterize the micro-damage. However, the research on micro-damage characterization using nonlinear Lamb wave imaging technique remains an ongoing challenge and is rarely reported. A method called standardized amplitude difference is proposed for nonlinear feature enhancement, and further for fatigue crack imaging based on the wavefield data. Wavefield data contain abundant information on the spatial and temporal variation of propagating waves in the damaged structure. The nonlinearity index ß' of the signal difference under the high and low incident wave amplitudes is calculated for fatigue crack imaging. Two scanning methods, including local scanning and global scanning, are introduced to image the fatigue crack tip and visualize the wave field of the harmonics respectively. The experimental validation, based on the imaging results of an aluminum alloy plate specimen with a barely visible fatigue crack and a steel plate with a blind hole, manifests that the proposed method can be used to enhance and extract the nonlinear features and suppress the fundamental frequency, so as to improve the signal-to-noise ratio (SNR) of the micro-damage imaging results.

A multiscale residual U-net architecture for super-resolution ultrasonic phased array imaging from full matrix capture data.

Ultrasonic phased array imaging using full-matrix capture (FMC) has raised great interest among various communities, including the nondestructive testing community, as it makes full use of the echo space to provide preferable visualization performance of inhomogeneities. The conventional way of FMC data postprocessing for imaging is through beamforming approaches, such as delay-and-sum, which suffers from limited imaging resolution and contrast-to-noise ratio. To tackle these difficulties, we propose a deep learning (DL)-based image forming approach, termed FMC-Net, to reconstruct high-quality ultrasonic images directly from FMC data. Benefitting from the remarkable capability of DL to approximate nonlinear mapping, the developed FMC-Net automatically models the underlying nonlinear wave-matter interactions; thus, it is trained end-to-end to link the FMC data to the spatial distribution of the acoustic scattering coefficient of the inspected object. Specifically, the FMC-Net is an encoder-decoder architecture composed of multiscale residual modules that make local perception at different scales for the transmitter-receiver pair combinations in the FMC data. We numerically and experimentally compared the DL imaging results to the total focusing method and wavenumber algorithm and demonstrated that the proposed FMC-Net remarkably outperforms conventional methods in terms of exceeding resolution limit and visualizing subwavelength defects. It is expected that the proposed DL approach can benefit a variety of ultrasonic array imaging applications.

An optimized total focusing method based on delay-multiply-and-sum for nondestructive testing.

Total focusing method (TFM) attracts much interest because of high image resolution and large inspection coverage. However, the synthetic focusing approach based on delay-and-sum beamforming employs only the defect information contained in the dataset while ignoring the spatial information of the array signals, leading to limited imaging performance mixed with artifacts and noise. In addition, the signal-to-noise ratio (SNR) suffers due to single-element emission of full matrix capture. This work combines a modified delay-multiply-and-sum (DMAS) beamforming approach with conventional synthetic focusing in the TFM algorithm, to achieve optimization of TFM imaging performance. DMAS-based TFM is able to take full advantage of the defect and spatial information in the array dataset, and to generate new frequency components for better image reconstruction. As demonstrated on a series of comparative simulation and experimental results, the imaging results of the optimized TFM provide a considerable improvement in SNR. Better lateral spatial resolution is also achieved due to the increased number of equivalent transducer elements and second harmonic component. Therefore, this work provides a quite promising alternative solution for the post-processing of ultrasonic phased array with improved imaging performance.

Autonomous characterization of grain size distribution using nonlinear Lamb waves based on deep learning.

Characterization of grain microstructures of metallic materials is crucial to materials science and engineering applications. Unfortunately, the universal electron microscopic methodologies can only capture two-dimensional local observations of the microstructures in a time-consuming destructive way. In this regard, the nonlinear ultrasonic technique shows the potential for efficient and nondestructive microstructure characterization due to its high sensitivity to microstructural features of materials, but is hindered by the ill-posed inverse problem for multiparameter estimation induced by the incomplete understanding of the complicated nonlinear mechanical interaction mechanism. We propose an explainable nonlinearity-aware multilevel wavelet decomposition-multichannel one-dimensional convolutional neural network to hierarchically extracts multilevel time-frequency features of the acoustic nonlinearity and automatically model latent nonlinear dynamics directly from the nonlinear ultrasonic responses. The results demonstrate that the proposed approach establishes the complex mapping between acoustic nonlinearity and microstructural features, thereby determining the lognormal distribution of grain size in metallic materials rather than only average grain size. In the meantime, the integration of the designed nonlinearity-aware network and the quantitative analysis of component importance provides an acceptable physical explainability of the deep learning approach for the nonlinear ultrasonic technique. Our study shows the promise of this technique for real-time in situ evaluation of microstructural evolution in various applications.

Nondestructive Evaluation of Composite Bonding Structure Used in Electrical Insulation Based on Active Infrared Thermography.

Nondestructive testing and evaluation of composite insulating components of electrical equipment is extremely necessary for assuring the safety of a power system. However, most existing nondestructive testing methods are not applicable for fast and effective live detection due to their time-consuming operation, high cost, and contact or near-field measurement. In this work, the effectiveness of active infrared thermography was investigated for detecting defects in silicone rubber (SIR)-fiber-reinforced plastic (FRP) bonding structures, which have been commonly used in insulating components of power equipment. The effectiveness of differential thermal image for enhancing the contrast of defective and sound areas and eliminating additive noise was demonstrated. Particularly, frame difference thermal image obtained by subtracting two differential thermal images extracting from respectively before and after the contrast inversion was proposed to enhance defect identification. The results revealed that defects of various sizes and depths such as voids, cracks, and interface disbonding of the SIR-FRP bonding structure were accurately detected by thermographic data. With the advantages of a quick and simple process, safety, universal applicability, visual results, far-field measurement, and quantitative defect estimation capabilities, active infrared thermography would be quite promising for live detection of electrical equipment.

Linear stability analysis of magnetohydrodynamic duct flows with perfectly conducting walls.

The stability of magnetohydrodynamic flow in a duct with perfectly conducting walls is investigated in the presence of a homogeneous and constant static magnetic field. The temporal growth and spatial distribution of perturbations are obtained by solving iteratively the direct and adjoint governing equations with respect of perturbations, based on nonmodal stability theory. The effect of the applied magnetic field, as well as the aspect ratio of the duct on the stability of the magnetohydrodynamic duct flow is taken into account. The computational results show that, weak jets appear near the sidewalls at a moderate magnetic field and the velocity of the jet increases with the increase of the intensity of the magnetic field. The duct flow is stable at either weak or strong magnetic field, but becomes unstable at moderate intensity magnetic field, and the stability is invariance with the aspect ratio of the duct. The instability of magnetohydrodynamic duct flow is related with the exponential growth of perturbations evolving on the fully developed jets. Transient growth of perturbations is also observed in the computation and the optimal perturbation is found to be in the form of streamwise vortices and localized within the sidewall layers. By contrast, the Hartmann layer perpendicular to the magnetic field is irrelevant to the stability issue of the magnetohydrodynamic duct flow.