Resonant Airborne Acoustic Emission for Nondestructive Testing and Defect Imaging in Composites

A new version of an acoustic emission mode which is different from its traditional counterpart is discussed in view of applications for nondestructive testing. It is based on the effect of acoustic waves generation from the defect area in ambient air by local standing wave vibration developed in this area at the defect resonant frequency. Another approach which does not require preliminary knowledge of local defect-resonance frequency is one that uses wideband acoustic activation by a noise-like input signal. The acoustic emission field from the defect area is a “fingerprint” of the radiation source, and thus is applicable to defect detection and imaging. This enables the use of commercial microphone scanning for detecting and imaging various defects in composites. An improvement in the acoustic-emission scanning mode based on a multiple-axis robot is studied to applications to complex shape components. A rapid, full-field imaging of the acoustic-emission field is implemented by means of an array of microphones (acoustic camera). Numerous case studies validate the potential of the resonant acoustic-emission modes for integration in the defect imaging system based on inexpensive, fully acoustic instrumental components.

Robust Guided Wave Tomography Method for Large and Irregular Defects

Finding a fast, robust way to quantitatively measuring the remaining wall thickness of complex structures when multiple defects exist is one of the leading challenges in Nondestructive Testing (NDT). Traditional inversion algorithms like ray tomography and full waveform inversion (FWI) suffered from problems like convergence, limited resolution and slow speed. Diffraction tomography (DT) has speed advantage over the preceding methods and its resolution can be further amplified by integrating with other methods like bent-ray tomography and iteration. However, DT can only detect shallow and small defects. Compared with those methods, convolutional neural network (CNN) opens a new way for quantitative defect imaging, as with pre-trained data it can achieve significant speed and resolution than the traditional methods. In this paper, we investigated the performance of CNN in imaging multiple defects and the inversion results show that when dealing with multiple defects with complex shape on a plate-like structure, CNN can achieve better resolution than other methods with maximum errors below 0.54mm in most regions. This research provides the experimental guidance for future study in finding the possible ways to improve the resolution of the algorithms.

Statistical Analysis of Guided Wave Imaging Algorithms Performance Illustrated by a Simple Structural Health Monitoring Configuration

A guided wave-based structural health monitoring (GW-SHM) system aims at determining the integrity of a wide variety of plate-like structures such as aircraft fuselages, pipes, and fuel tanks. It is often based on a sparse grid of piezoelectric transducers for exciting and sensing GWs that under certain conditions interact with damage while propagating. In recent years, various defect imaging algorithms have been proposed for processing GWs signals and, particularly, for computing an image representing the integrity of the studied structure. The performance of the GW-SHM system highly depends on a signal processing methodology. This paper compares defect localization accuracy of the three state-of-art defect imaging algorithms (delay-and-sum, minimum variance, and excitelet) applied to an extensive simulated database of GWs propagation and GWs-defect interaction in aluminum plate under varying temperature and transducers degradation. This study is conducted in order to provide statistical inferences, essential for SHM system performance demonstration.

Numerical simulation of internal defect imaging with the longitudinal wave generated by laser induced ultrasonic array

To verify the performance of longitudinal waves induced by laser phased arrays (LPA) for detection and quantitative evaluation in internal defects, the finite element method (FEM) is utilized to establish the models of LPA scanning processing. The interaction of longitudinal wave and internal defect is analyzed. Besides, the two components of the reflected longitudinal waves (the longitudinal wave component [Formula: see text] and the shear wave component [Formula: see text] are focused on the imaging of defects with the synthetic aperture focusing technique (SAFT) and total focusing method (TFM) algorithms. It shows that the imaging of internal sub-millimeter defect is obtained using the LPA. The defect size and location are simultaneously calculated, with the relative error being 6.7% and 2.9%, respectively. The proposed longitudinal wave-based LPA is a promising method for the imaging and evaluation of internal micro defects.

A Defects Localization Algorithm Based on the Lamb Wave of Plate Structure

Defect imaging algorithms play an important role in Lamb waves based researches of nondestructive testing (NDT) and structural health monitoring (SHM). In classical algorithms, the location or distribution of defects is visualized through mapping the amplitude or phase information of signals gotten by multiple inspection pairs from the time domain to every discrete spatial grid of plates. It is time-consuming in the detection of plates with large size and many transducers. Transforming the defect imaging problem into a scattering source search problem, an intelligent defect localization algorithm was proposed for NDT and SHM with the Lamb waves and sparse array. In the algorithm, the elliptic trajectory-dependent individuals of every inspection pair were extracted first, then the defect position was identified by analyzing the distribution of individuals these located at the intersection of multiply elliptic trajectories. Considering the fuzzy and diversity characteristics in the detection of defects, a fuzzy control parameter and an adaptive individual updating strategy based on the k-means algorithm were introduced to ensure the robustness of the algorithm. The effectiveness of the proposed algorithm was verified by numerical models and experiments. The influences of the fuzzy control parameter and the individual updating strategy on the performance of the algorithm were analyzed furthermore.