Ultrasonic Imaging Method Research Articles

The numerical reconstruction methods for characterizing defects by full matrix data using an ultrasonic array

Abstract This study introduces ultrasonic imaging and inversion methods for the quantitative characterization of crack defects. The Total Focusing Method (TFM) is a high-precision ultrasonic imaging method, but it may produce length errors when used for large-angle defect detection. An SV transverse wave TFM method is introduced, significantly reducing length errors in a 60° crack to 8.31%. However, the ultrasonic image method is influenced by factors like array position, limiting its practicality. To overcome this limitation, the study proposes an inversion method using the subarray scattering coefficient matrix, which is more stable and less sensitive to array positioning. Comparative analysis with finite element simulations and experiments indicates optimal array positions associated with different methods. At these positions, both methods yield angular characterization errors for the 60° crack of less than 1%, validating the proposed techniques for non-destructive testing applications.

Adaptive total focusing method for ultrasonic full-matrix imaging of anisotropic CFRP laminates

Abstract The application of total focusing method (TFM) for imaging of Carbon fiber reinforced plastics (CFRP) with ultrasonic phased array is particularly challenging due to the layered structure and anisotropy of CFRP. To improve the TFM imaging performance, the precise knowledge of complex ultrasonic paths in anisotropic CFRP laminates should be obtained for accurate calculation of time of flights (TOFs), which makes the inspection process complicated and time-consuming. In this work, an adaptive TFM is developed for full-matrix imaging of CFRP laminates using ultrasonic phased array. Based on the acquired full matrix data, the elastic constants, including C 11, C 22, C 55, and C 13, can be directly derived using TOFs of A-scan signals. Based on the measured elastic constants, group velocity profiles of quasi-pressure (QP) wave were then calculated for different stacking sequences, from which the TOFs were corrected. To verify the effectiveness of proposed method, simulations and experiments are demonstrated on the CFRP laminates with different stacking sequences. As a representative example, the delamination defects are showcased. Compared with isotropic TFM, the SNR of proposed method is increased by 10.2-18.0 dB. The simulated and experimental results agreed well with each other, indicating that the feasibility of proposed method in full matrix imaging of anisotropic CFRPs.

Research on Phased Array Ultrasonic Imaging Method Based on Time Reversal Theory

Abstract With the wide application of additive manufacturing components and composite materials, it is of great significance to detect defects with non-destructive testing methods in the material manufacturing process and in service. Due to the strong attenuation and inhomogeneity characteristics, it is inefficient and poor imaging results to detect its internal defects by conventional ultrasonic testing methods. To improve the detection accuracy, this paper proposes an improved imaging algorithm with time reversal-total focusing method (TR-TFM) imaging technology to improve the defects detection in strongly attenuating materials. The experimental results show that the amplitude of the defect signal is increased by 8.30 dB, and compared with total focusing method (TFM), the diameter of the detected defect with TR-TFM imaging is increased from 1.20 mm to 1.60 mm, which is more adjacent to the accurate size. Meanwhile, with the sparse matrix based on FMC data, TR-TFM could obtain the same image quality with the approximate time of TFM imaging.

An efficient ultrasonic wavenumber-domain plane wave imaging method towards the inspection of curved structures

Ultrasonic phased array testing is commonly employed for inspecting curved structures. Conventional plane wave imaging techniques, based on delay-and-sum in the time-domain, offer high image quality and inspection accuracy but suffer from low frame rates due to their high computational complexity. In this work, an efficient wavenumber-domain imaging method that combines non-stationary wavefield extrapolation and f-k migration is proposed for curved structure inspection. Special emission focal laws are designed to generate a sequence of steered plane waves through the curved interface. The raw data is then extrapolated to the top boundary of the region of interest, followed by f-k migration to reconstruct images with high time efficiency. Simulation and experimental evaluations demonstrate a time reduction by a factor of up to 32.24 compared to conventional time-domain plane wave image reconstruction with equivalent image quality, highlighting its potential for monitoring flaws in real-time.

A novel method of ultrasonic tomographic imaging of defects in the coating layer by image fusion and binarization techniques

A novel method of ultrasonic tomographic imaging of defects in the coating layer by image fusion and binarization techniques

Ultrasonic diffuse bulk wave passive array imaging of internal defects in a complex structure

Ultrasonic bulk wave inspection of defects in safety–critical components with complex external geometries, such as turbine blades is challenging. While ultrasonic phased array imaging can yield high-resolution subsurface images, a commercial phased array probe can hardly be mounted on irregular external boundaries to perform in-situ imaging. In fact, a component with irregular shapes, as a highly reverberant body, is capable of generating elastic random diffuse or coda wavefields. The diffuse wavefields can be utilized to reconstruct Green’s functions between any two passive receiving points. In this paper, an ultrasonic passive array imaging method using the diffuse reverberation resulting from complex boundaries is implemented to image internal defects. The method involves the utilization of active piezoelectric actuators to excite elastic diffuse waves within the component, which are received by a laser vibrometer scanning at multiple points. A passive full matrix capture (FMC) of array signals is extracted for defect imaging using the total focusing method. The proposed method is evaluated by the numerical simulations, and the effects of centre frequency, bandwidth, and source excitation methods on the imaging performance are investigated. An experiment using a turbine blade-like structure is conducted to further evaluate the imaging method.

Ultrasonic phased array imaging of multi-layered structures using full-matrix migration and normalized cross-correlation matching technique

Multi-layered structures play a critical role in aerospace, automotive and electrical industries, so the non-destructive evaluation and maintenance of such structures must be accurate and rapid. The application of conventional ultrasonic phased array imaging methods is limited due to computational complexity, image quality and image size. In this paper, we proposed a cost-effective, high-quality imaging method that uses full-matrix migration total focus method (FM-TFM) and produces large-area scanned image with the aid of normalized cross-correlation (NCC) template matching technique. The FM-TFM method eliminated the need for iterative refraction point calculations by using full matrix migration extrapolation, and the optimization of NCC was also investigated in the study. The verification was divided into two parts, fixed transducer experiment and manual scanned experiment, to demonstrate the advantages of FM-TFM with improved SNR (22 % or greater) and one order of magnitude reduction in computational time when compared to ray tracing total focus method (RT-TFM) and NCC matching accuracy of multilayer specimen, respectively. The proposed method can help eliminate the need for costly scanning equipment and shows great potential for extension to other TFM-based approaches.

An efficient ultrasonic full-matrix imaging method for industrial curved-surface components defect detection

Curved-surface components are ubiquitous in various industries. However, defects inside the components badly degenerate the mechanical properties and lead to premature failure. Ultrasonic full-matrix imaging is a prominent method in nondestructive testing, but there is still a lack of efficient imaging methods for cases with curved interfaces. To amend this problem, this article proposes an efficient ultrasonic full-matrix imaging method to detect the defects in the industrial curved-surface components. The wavefield extrapolation for full matrix capture (FMC) dataset is taken as the combined effect of the downward migration of the source and receiver wavefields in the wavenumber-frequency domain. Compensation terms corresponding to the source and receiver wavefields act on the wavefield of FMC in the spatial-frequency domain to make up for lateral variations of the sound speed. After the wavefield reconstruction, an efficient imaging condition is implemented to transform the wavefield into the defect image. The best compensation term is determined and the influence factors is investigated in the simulation experiments. Laboratory experiments furtherly validate the superiority of the proposed method over the existing methods, especially for the imaging efficiency which is twice of the phase shift migration (PSM) based method.

Characterisation of coherent ultrasonic nonlinear imaging

Nonlinear ultrasonic imaging methods for non-destructive testing have shown sensitivity to early damage formation. Among those, the Fundamental wave Amplitude Difference (FAD) technique has been proposed in the literature as a nonlinearity mapping method for defect visualisation. It does so by calculating the residual between a low and high nonlinearity image, which is achieved by some modulation of the focal spot amplitude. This is realised either through different input voltages or alternating the element-wise firing scheme. This paper assesses the sensitivity of FAD through different techniques used for the generation of the low nonlinearity images, using both theoretical and experimental approaches. These techniques include (1) Different-Voltage FAD (DV-FAD); (2) Odd-Even FAD (OE-FAD) and; (3) Parallel–Sequential FAD (PS-FAD). Formulae are derived that allow the different firing sequences and image corrections to be compared. The FAD techniques are then experimentally evaluated against the theory, showing a 50% amplitude gain at the crack tip when comparing PS-FAD to OE-FAD and DV-FAD. Additionally, the instrument behaviour is assessed by considering the image correction required to improve the image contrast for the visualisation of the crack tip. Finally, it is shown that all FAD techniques are equivalent in terms of nonlinear detection but additional work is required in comparing the performance of other array controllers when using FAD.

Investigation of correlation between shear wave elastography and lymphangiogenesis in invasive breast cancer and diagnosis of axillary lymph node metastasis

BackgroundAccurate evaluation of axillary lymph node metastasis (LNM) in breast cancer is very important. A large number of hyperplastic and dilated lymphangiogenesis cases can usually be found in the pericancerous tissue of breast cancer to promote the occurrence of tumor metastasis.Shear wave elastography (SWE) can be used as an important means for evaluating pericancerous stiffness. We determined the stiffness of the pericancerous by SWE to diagnose LNM and lymphangiogenesis in invasive breast cancer (IBC).MethodsPatients with clinical T1-T2 stage IBC who received surgical treatment in our hospital from June 2020 to December 2020 were retrospectively enrolled. A total of 299 patients were eventually included in the preliminary study, which included an investigation of clinicopathological features, ultrasonic characteristics, and SWE parameters. Multivariable logistic regression analysis was used to establish diagnostic model and evaluated its diagnostic performance of LNM. The correlation among SWE values, collagen volume fraction (CVF), and microlymphatic density (MLD) in primary breast cancer lesions was analyzed in another 97 patients.ResultsThe logistic regression model is Logit(P)=-1.878 + 0.992*LVI-2.010*posterior feature enhancement + 1.230*posterior feature shadowing + 0.102*posterior feature combined pattern + 0.009*Emax. The optimum cutoff value of the logistic regression model was 0.365, and the AUC (95% CI) was 0.697 (0.636–0.758); the sensitivity (70.7 vs. 54.3), positive predictive value (PPV) (54.0 vs. 50.8), negative predictive value (NPV) (76.9 vs. 69.7), and accuracy (65.2 vs. 61.9) were all higher than Emax. There was no correlation between the SWE parameters and MLD in primary breast cancer lesions.ConclusionsThe logistic regression model can help us to determine LNM, thus providing more imaging basis for the selection of preoperative treatment. The SWE parameter of the primary breast cancer lesion cannot reflect the peritumoral lymphangiogenesis, and we still need to find a new ultrasonic imaging method.

Deep learning based ultrasonic reconstruction of rough surface morphology

This paper introduces a methodology to recover the morphology of a complex rough surface from ultrasonic pulse echo measurements with an array of equidistant sensors using the one dimensional convolution neural network (1DCNN). The neural network is trained by the datasets simulated from high-fidelity finite element simulations for surfaces with a range of roughness parameters and is tested on both numerical and real experimental data. To assess the performance of our proposed method, the rough surface reconstruction results from the deep learning approach are compared with those obtained from conventional ultrasonic array imaging methods. Unlike array imaging-based methods that require a large number of sensors (e.g., 128, 64 or 32), the deep learning-based method uses pulse echo signals and can achieve accurate results with much fewer sensors. The developed deep learning approach has the potential to enable low-cost, accurate, and real-time reconstruction of complex surface profiles.

Convolutional neural network and level-set spectral element method for ultrasonic imaging of delamination cavities in an anisotropic composite structure

We present a computational approach that incorporates a convolutional neural network (CNN) for detecting internal delamination in a layered 2D plane-strain anisotropic composite structure of transient elastodynamic fields. The two-dimensional spectral element method (SEM) is utilized to simulate the propagation of elastic waves in an orthotropic solid sandwiched by isotropic solids and their interaction with the internal delamination cavity. This work generates training data consisting of input-layer features (i.e., measured wave signals) and output-layer features (i.e., element types, such as void or regular, of all elements in a domain).To accelerate training data generation, we utilize explicit time integration (e.g., the Runge–Kutta scheme) coupled with an SEM wave solver. Applying the level-set method additionally avoids having to perform an expensive re-meshing process for every possible configuration of the delamination cavities during the data-generation phase. The CNN is trained to classify each element as a non-void or void element from the measured wave signals. Clusters of identified void elements reconstruct targeted cavities. Once our neural network is trained using synthetic data, we analyze how effectively the CNN performs on synthetic measurement data. To this end, we use blind test data from a third-party simulator that explicitly models the traction-free boundary of cavities for anisotropic materials without the application of the level-set method.Our numerical examples show that our approach can effectively detect the internal cavities in an anisotropic structure made of aluminum and carbon fiber-reinforced epoxy using the measured elastic waves without any prior information about the cavities’ locations, shapes, and numbers. The presented method can be extended into a more realistic 3D setting and utilized for the nondestructive test of various anisotropic composite structures.

Joint learning of sparse and limited-view guided waves signals for feature reconstruction and imaging

Sparse and limited-view ultrasonic guided wave imaging has become a research hotspot in the field. Studies have shown that traditional under-sampling ultrasonic imaging methods either require a significant amount of time to recover the full data or produce poor quality imaging results. To address these issues, this paper proposes an end-to-end ultrasonic guided wave joint learning imaging method for sparse and limited-view transducer arrays, which integrates sparse feature reconstruction and deep learning imaging methods. Numerical and experimental studies demonstrate that this approach significantly improves the quality of imaging results. The quality of imaging results for sparse and limited-view transducer arrays is evaluated and quantified using average correlation coefficients on the testing set. The feasibility and effectiveness of the proposed method have been verified.

Discretized tensor-based model of total focusing method: A sparse regularization approach for enhanced ultrasonic phased array imaging

The total focusing method (TFM) is considered as the standard in ultrasonic phased array imaging and plays a vital role in industrial non-destructive testing (NDT). By utilizing the full matrix capture (FMC) dataset, the TFM can focus on every point within the specified imaging region, and is more accurate than the traditional ultrasonic phased array imaging methods. However, the TFM is essentially a delay and sum technique that often operates linearly on the time-domain signals and takes no prior information into account, so its image quality remains inadequate when dealing with defects in close proximity or scattering materials. To address this problem, this paper formulates the imaging principle of the TFM as a Boolean matrix and establishes the discretized tensor-based model. Subsequently, the model is addressed by employing the sparse regularization strategy, taking into some characteristics of industrial NDT. Regarding the solution algorithm, due to the generation of negative values by the fast iterative shrinkage threshold algorithm (FISTA), this paper introduces the rectified linear unit (ReLU) function as a non-negative constraint and presents a dedicated solution algorithm (ReLU-FISTA) for acquiring detection results. Through verification of simulation and experiment, the proposed approach exhibits superior capabilities of defect characterization and noise suppression when compared to the TFM, leading to substantial enhancements in image quality.

Longitudinal 3-D Visualization of Microvascular Disruption and Perfusion Changes in Mice During the Evolution of Glioblastoma Using Super-Resolution Ultrasound.

Glioblastoma is an aggressive brain cancer with a very poor prognosis in which less than 6% of patients survive more than 5 years post-diagnosis. The outcome of this disease for many patients may be improved by early detection. This could provide clinicians with the information needed to take early action for treatment. In this work, we present the utilization of a noninvasive, fully volumetric ultrasonic imaging method to assess microvascular change during the evolution of glioblastoma in mice. Volumetric ultrasound localization microscopy (ULM) was used to observe statistically significant (p<0.05) reduction in the appearance of functional vasculature over the course of three weeks. We also demonstrate evidence suggesting the reduction of vascular flow for vessels peripheral to the tumor. With an 82.5% consistency rate in acquiring high-quality vascular images, we demonstrate the possibility of volumetric ULM as a longitudinal method for microvascular characterization of neurological disease.

Research on the 3D Reverse Time Migration Technique for Internal Defects Imaging and Sensor Settings of Pressure Pipelines.

Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings.

Evaluation of quantitative ultrasonic C-scan testing for refill friction stir spot welding joints based on time-frequency analysis

This paper focuses on a comparative study of different ultrasonic feature-based C-scan testing imaging methods for characterizing refill friction stir spot welding (RFSSW) joints, and accurately identifying and measuring the nugget boundary. The aim is to explore a suitable method for nugget characterization and automatic size analysis. The research indicates that the frequency-domain C-scan imaging method outperforms the time-domain C-scan imaging method in accurately characterizing the nugget. Furthermore, the C-scan imaging method based on the feature value of the base material zone (BMZ) echo signal performs better than the method based on the nugget echo signal. The tested nugget sizes obtained by using the –6 dB drop-off method to identify the boundaries in the C-scan images are generally larger than the metallographic measurement values. A novel method is proposed in this paper, using the difference in main frequency amplitude values between the BMZ and the nugget as the feature value for the C-scan testing image, and employing the Hough circle transformation for an automatic extraction of joint size. This method achieves improved nugget characterization and higher accuracy in size analysis.

Benefits of software beamforming for ultrasonic inspections

Nondestructive testing (NDT) is used in more and more industries and inspection techniques must constantly adapt to complex materials (coarse grained materials, composites, etc.), defects (corrosion, fatigue crack, HTHA, etc.) or new inspection standards. Ultrasonic imaging methods have long been limited by low data throughput and a small number of transducers, but this is no longer the case with nowadays electronic devices and computers which enable large amounts of raw data to be acquired and processed in real time. The Delay-and-Sum (DAS) algorithm, which has been used almost automatically in many applications over the past few years, is now outperformed by advanced processing methods. In this context, this article presents few real-time advanced reconstruction techniques that enhance the quality of ultrasonic images on typical industrial applications. The first example concerns coherence beamformers such as Phase Coherence Imaging (PCI) which exploit the phase information of the signals. This type of method proves to be highly effective in detecting diffraction echoes from crack-like defects or in improving contrast and resolution of images from corroded samples inspections. The second example uses cross-correlation techniques that have been known for years to improve detection in various fields. Correlating the data with the transmitted signal reduces electronic noise and improves signal-to-noise ratio. This application also benefits from recent progress of ultrasonic devices that are able to modulate complex excitation signals in emission. This type of method is very promising for the inspection of attenuative materials and coarse-grained steels.

Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decomposition

Condition monitoring of high-density polyethylene (HDPE) pipes used for fluid and gas transfer is important for the safety of energy conservation and the environment. Ultrasonic phased array imaging methods provide a solution to detect and assess defects in HDPE pipes. However, ultrasonic bulk waves propagating in these viscoelastic media are strongly attenuated, resulting in reduced signal amplitude. In this study, a linear-phase Finite Impulse Response (FIR) filter is used to remove unwanted frequency components from the measured ultrasonic signals to improve the signal-to-noise ratio before applying the imaging algorithm of the total focusing method (TFM). Building upon this, a block-wise singular value decomposition (SVD) technique, which can adaptively determine the singular value cutoff threshold based on each block in the whole TFM image, is used to enhance the obtained TFM image quality. The performance of the combination of FIR filtering and block-wise SVD technique is validated by experimental data of HDPE pipe materials. Results demonstrate that the proposed approach generates good images to provide the detection and characterization of side-drilled holes in HDPE pipe materials.

Time- Versus Frequency-Domain Inverse Elastic Scattering: Theory and Experiment

This study formally adapts the time-domain linear sampling method (TLSM) for ultrasonic imaging of stationary and evolving fractures in safety-critical components. The TLSM indicator is then applied to the laboratory test data of [F. Pourahmadian and H. Yue, Inverse Problems, 37 (2021), 055012; F. Pourahmadian, Mech. Syst. Signal Process., 161 (2021), 108029] and the obtained reconstructions are compared to their frequency-domain counterparts. The results highlight the unique capability of the time-domain imaging functional for high-fidelity tracking of evolving damage, and its relative robustness to sparse and reduced-aperture data at moderate noise levels. A comparative analysis of the TLSM images against the multifrequency LSM maps of [F. Pourahmadian and H. Yue, Inverse Problems, 37 (2021), 055012] further reveals that thanks to the full-waveform inversion in time and space, the TLSM generates images of remarkably higher quality with the same dataset.