This paper describes an imaging method for near-field defect detection in aluminum plates based on Green’s function recovery and application of instantaneous phase coherence weighting factors. The directly acquired acoustic information of near-field defects is usually obscured by the nonlinear effects due to the physical limitation of the acquisition system. Using the diffuse field to recover the Green’s function can effectively retrieve the early time information. However, averaging operations of finite number in this process produces an imperfect imaging result. In order to improve the image quality, two kinds of instantaneous phased coherence weighting factors are used to weight the Green’s function to reduce the background noise and improve the signal-to-noise ratio: the instantaneous phase coherence factor (IPCF), and the instantaneous phase weighting factor (IPWF). Experiments are conducted on two aluminum plates with two and four near-field defects, respectively. As a result, the background noise of amplitude images weighted by IPCF and IPWF is less than that of the conventional total focusing method (TFM). In addition, the IPCF image achieves a better signal-to-noise ratio (SNR) than that of IPWF, and the phase discontinuity in an IPWF image is suppressed through the IPCF.

Out-of-plane waviness is one of the most common defects which degenerates the strength, stiffness, and fatigue life of hybrid glass–carbon fiber–reinforced polymer composites (FRPs). An accurate and high-speed non-destructive testing method is highly desired for large composite structures in industries. Ultrasonic phased array is a great candidate for such application. This paper applies the wavenumber algorithm to image the waviness in hybrid FRP plates which are a multi-layered medium. The central frequency of 5 MHZ is chosen in order to maximize the ply resonance. Transducers are migrated virtually to each interface between glass and carbon plies in order to overcome the difficulty of wave propagation analysis in such multi-layered system. The wavenumber algorithm demonstrates a better computational performance compared to that of the traditional total focusing method (TFM) in time domain up to 6 times. The glass ply depth and waviness angle can be more accurately presented with relative errors less than 1.5% and 14.8%, respectively. In addition, the resin-rich defect characterization is also achievable with a maximum error of 14.4%.

Ultrasonic phased array is widely used for damage detection recently because of its high sensitivity and rapid scanning without sensor movements. However, the measured signal is always influenced by the remnants of the initial excitation and the nonlinear signals from the instrumentation, which limits its application in thin-plate structures. To address this issue, an approach called auto-correlation subtraction is proposed to extract the scattering information of defects in this paper. In order to testify the feasibility of this method for damage detection, the experiments were carried out on three thin aluminum plates combined with the total focusing method (TFM) for imaging. By auto-correlating the full matrix data received by sensors and then subtracting the average auto-correlation of noise recorded by all receivers, the coherent scattered signal containing defect information is recovered. The experimental results indicate that the coherent travel time is in agreement with the theoretical value and the signal-to-noise ratio are improved. Additionally, compared with the cross-correlation technique, the time synchronization between different receivers is not necessary with the auto-correlation method. Results indicate that the presented method can improve the imaging resolution and has a great potential in the field of non-destructive testing.

In this paper, phase coherence imaging is proposed to improve spatial resolution and signal-to-noise ratio (SNR) of near-surface defects in rails using cross-correlation of ultrasonic diffuse fields. The direct signals acquired by the phased array are often obscured by nonlinear effects. Thus, the output image processed by conventional post-processing algorithms, like total focus method (TFM), has a blind zone close to the array. To overcome this problem, the diffuse fields, which contain spatial phase correlations, are applied to recover Green’s function. In addition, with the purpose of improving image quality, the Green’s function is further weighted by a special coherent factor, sign coherence factor (SCF), for grating and side lobes suppression. Experiments are conducted on two rails and data acquisition is completed by a commercial 32-element phased array. The quantitative performance comparison of TFM and SCF images is implemented in terms of the array performance indicator (API) and SNR. The results show that the API of SCF is significantly lower than that of TFM. As for SNR, SCF achieved a better SNR than that of TFM. The study in this paper provides an experimental reference for detecting near-surface defects in the rails.