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In this article, we study a machine learning-based approach for characterising crack-like defects in specimens with complex geometries using the multi-modal total focusing method (M-TFM) applied to ultrasonic full matrix capture (FMC) data. In particular, this work frames into a scientific machine learning perspective leveraging the use of physics-based numerical solvers as an efficient yet accurate data augmentation strategy. Toward this end, the numerical simulations have been tailored to built a suitable data set aiming at gathering the uncertainties in M-TFM images encountered when the reconstruction parameters are barely known. That is, the use of such a physical knowledge has been exploited to train a deep convolutional neural network designed for retrieving crack size and position based on multi-modal TFM images. Furthermore, for validating the learning proposed scheme, an experimental campaign has been also carried out accordingly to the most relevant factors that typically impact the quality of reconstructed M-TFM images. The developed approach has been tested on both synthetic and experimental data sets never shared in the training phase. [ABSTRACT FROM AUTHOR]

Large complex rings are widely used in wind power energy, aerospace, large construction machinery. There are different scales of defects in the manufacturing process for large complex rings. Because of the complex cross-section and large thickness, it is difficult to quantitative evaluation of defects by conventional ultrasonic technology. Therefore, the ultrasonic total focusing method (TFM) defect quantitative inspection for large complex rings is proposed in this paper. First, the TFM inspection technology of large complex rings for simultaneous scanning of the end-face and side-face is designed. Then, the maximum amplitudes of defects are collected at each grid point of the imaging area by the TFM inspection of calibration specimens. The Φ1.5mm defect quantitative evaluation mappings are obtained by the interpolation fitting algorithm. Finally, the ultrasonic TFM and B-scan inspection experiments of the Φ3440mm large ring are carried out. The results show that the TFM quantitative evaluation mapping has higher accuracy compared to the conventional B-scan method. Especially for the Φ1.5mm defects, the average error of TFM quantitative evaluation is 2.92%. The proposed method can effectively identify defects of other sizes that exceed the calibration mapping. This work has industrial application value for developing ultrasonic TFM inspection of large complex components. [ABSTRACT FROM AUTHOR]

9% Ni steel is commonly utilised in liquefied natural gas storage tanks due to its strong resistance to corrosion and oxidation. However, the welds in these tanks are prone to serious defects, necessitating thorough non-destructive testing. Unfortunately, when inspecting nickel-based alloy welds using phased array ultrasonic testing (PAUT) technology the ultrasonic waves are scattered and attenuated because of the coarse columnar grains. To address this challenge, this paper introduces a based on SVD enhancement directivity corrected circular coherence factor weighted TFM imaging algorithm (SVD-DC-CCF). Initially, the CCF matrix is corrected by the directivity and energy attenuation compensation factors, then weighted with the TFM pixel point amplitude. Subsequently, the weighted amplitude matrix undergoes singular value decomposition (SVD), followed by the establishment of an enhancement factor to reconstruct the amplitude matrix. The proposed algorithm is evaluated through experiments on three types of defects in a 30.0 mm thick 9% Ni steel T-shaped fillet weld test block. Results demonstrate superior signal-to-noise ratio (SNR) and enhanced image resolution. Compared to TFM results, the SNR increases by 4.97 dB, 4.25 dB, and 4.96 dB, respectively, while the array performance index (API) decreases by 9.80%, 34.39%, and 22.22%, respectively. [ABSTRACT FROM AUTHOR]

Leaky Rayleigh wave testing has the advantages of non-contact, high sensitivity, and good beam directivity. However, current leaky Rayleigh wave testing mostly uses monolithic transducers and is seriously affected by acoustic wave attenuation and diffusion, resulting in poor image quality, limited detection range and low imaging efficiency. In this paper, a novel leaky Rayleigh wave testing method using an ultrasonic phased array is developed, and the total focusing method (TFM) is used to achieve high-quality imaging of defects. To improve imaging efficiency, the root-mean-square velocity (RMSV) model is utilised to rapidly acquire an approximate analytical solution for the time of flight of the acoustic wave. To obtain more accurate defect images further, the amplitude of the total focusing imaging signal is calibrated. The experimental results demonstrate that the proposed method achieves a 48% improvement in the imaging accuracy of a 1 mm hole, compared to the traditional Synthetic Aperture Focusing Technique (SAFT) imaging method. The proposed method achieves an 92% reduction in imaging time compared to the conventional TFM. The proposed method provides a new non-contact approach for high-accuracy and high-efficiency defect imaging using leaky Rayleigh wave. [ABSTRACT FROM AUTHOR]

In order to address the problem of poor imaging detection of internal defects caused by the multi-layer curved surface structure and strongly attenuating material of composite insulators, the total focusing method (TFM) with two-layer curved surface correction was investigated, Virtual source (VS) technology was introduced to improve the transmitted energy of the single-element transmission, combined with the delay multiply and sum (DMAS) algorithm to improve the signal-to-noise ratio (SNR) and ultrasonic imaging detection of defects of different sizes of side-drilled holes (SDHs) in the sheath layer and core rod layer. An examination ofthe delamination defects at the core rod layer of the composite insulators was also carried out. The results show that the double-layer surface correction method is able to calibrate the propagation time of the ultrasonic wave to accurately localise the defects. Moreover, the VS technique is able to effectively detect SDI-15 and delamination defects in the core rod layer that cannot be detected using the conventional TFM. After incorporating the DMAS technique, the average SNRs of the TFM and virtual source total focusing method (VSTFM) are improved by 12.75 dB and 13.77 dB, respectively. This shows that the DMAStechnique can significantly improve the SNR of detection. [ABSTRACT FROM AUTHOR]

High-density polyethylene (HDPE) pipes are widely used for urban natural gas transportation. Pipes are usually welded using the technique of thermal butt fusion, which is prone to manufacturing defects that are detrimental to safe operation. This paper proposes a spatiotemporal singular value decomposition preprocessing improved total focusing method (STSVD-ITFM) imaging algorithm combined with ultrasonic phased array technology for non-destructive testing. That is, the ultrasonic real-value signal data are first processed using STSVD filtering, enhancing the spatiotemporal singular values corresponding to the defective signal components. The TFM algorithm is then improved by establishing a composite modification factor based on the directivity function and the corrected energy attenuation factor by adding angle variable. Finally, the filtered signal data are utilized for imaging. Experiments are conducted by examining specimen blocks of HDPE materials with through-hole defects. The results show the following: the STSVD-ITFM algorithm proposed in this paper can better suppress static clutter in the near-field region, and the average signal-to-noise ratios are all higher than the TFM algorithm. Moreover, the STSVD-ITFM algorithm has the smallest average error among all defect depth quantification results. [ABSTRACT FROM AUTHOR]

The use of sparse matrix phased arrays can increase the speed of 3D-imaging of the internal structure of objects in ultrasonic nondestructive testing using the Total Focusing Method. The sparse transducer configuration determines the quality of the results. An application of Simulated Annealing for determining the configurations of sparse matrix phased arrays is discussed in terms of the restoration of high quality images of the internal structure of test objects. The performance of the configuration obtained by Simulated Annealing is verified by computer modeling. [ABSTRACT FROM AUTHOR]

The total focusing method (TFM) has been increasingly applied to weld inspection given its high image quality and defect sensitivity. Oblique incidence is widely used to steer the beam effectively, considering the defect orientation and structural complexity of welded structures. However, the conventional TFM based on the delay-and-sum (DAS) principle is time-consuming, especially for oblique incidence. In this paper, a fast full-matrix imaging algorithm in the Fourier domain is proposed to accelerate the TFM under the condition of oblique incidence. The algorithm adopts the Chebyshev polynomials of the second kind to directly expand the Fourier extrapolator with lateral sound velocity variation. The direct expansion maintains image accuracy and resolution in wide-angle situations, covering both small and large angles, making it highly suitable for weld inspection. Simulations prove that the third-order Chebyshev expansion is required to achieve image accuracy equivalent to the TFM with wide-angle incidence. Experiments verify the algorithm's performance for weld flaws using the proposed method with the transverse wave and the full-skip mode. The depth deviation is within 0.53 mm, and the sizing error is below 15%. The imaging efficiency is improved by a factor of up to 8 compared to conventional TFM. We conclude that the proposed method is applicable to high-speed weld inspection with various oblique incidence angles. [ABSTRACT FROM AUTHOR]

The total focusing method (TFM) is often considered to be the ‘gold standard’ for ultrasonic imaging in the field of nondestructive testing. The use of matrix phased arrays as probes allows for high-resolution volumetric TFM imaging. Conventional TFM imaging involves the use of full matrix capture (FMC) for ultrasonic signals acquisition, but in the case of a matrix phased array, this approach is associated with a huge volume of data to be acquired and processed. This severely limits the frame rate of volumetric imaging with 2D probes and necessitates the use of high-end equipment. Thus, the aim of this research was to develop a novel design method for determining the optimal sparse 2D probe configuration for specific conditions of ultrasonic imaging. The developed approach is based on simulated annealing and involves implementing the solution of the sparse matrix phased array layout optimization problem. In order to implement simulated annealing for the aforementioned task, its parameters were set, the acceptance function was introduced, and the approaches were proposed to compute beam directivity diagrams of sparse matrix phased arrays in TFM imaging. Experimental studies have shown that the proposed approach provides high-quality volumetric imaging with a decrease in data volume of up to 84% compared to that obtained using the FMC data acquisition method.

The total focusing method (TFM) has been increasingly applied to weld inspection given its high image quality and defect sensitivity. Oblique incidence is widely used to steer the beam effectively, considering the defect orientation and structural complexity of welded structures. However, the conventional TFM based on the delay-and-sum (DAS) principle is time-consuming, especially for oblique incidence. In this paper, a fast full-matrix imaging algorithm in the Fourier domain is proposed to accelerate the TFM under the condition of oblique incidence. The algorithm adopts the Chebyshev polynomials of the second kind to directly expand the Fourier extrapolator with lateral sound velocity variation. The direct expansion maintains image accuracy and resolution in wide-angle situations, covering both small and large angles, making it highly suitable for weld inspection. Simulations prove that the third-order Chebyshev expansion is required to achieve image accuracy equivalent to the TFM with wide-angle incidence. Experiments verify the algorithm’s performance for weld flaws using the proposed method with the transverse wave and the full-skip mode. The depth deviation is within 0.53 mm, and the sizing error is below 15%. The imaging efficiency is improved by a factor of up to 8 compared to conventional TFM. We conclude that the proposed method is applicable to high-speed weld inspection with various oblique incidence angles.

Robotic inspection of complex geometry components using immersion ultrasonic techniques is relevant for many ultrasonic Non-Destructive Testing (NDT) applications in different industries. To image large component, ultrasonic probe is manipulated around the component immersed in water using a robotic guided system to create a tiled image of the entire component. Due to the mechanical vibrations of the high-speed robotic arm, the probe position coordinates provided by the robotic system are not precise enough to ensure an accurate reconstruction (stitching) of a composite image from all individual images. In this work, we propose a stitching method specifically designed to create a single 2D image of the surface of an inspected component from a stack of Total Focusing Method (TFM) images. This stitching method uses Iterative Closest Point (ICP) registration to estimate a 2D rigid transformation between two consecutive 2D images, by maximizing the overlap between the surface geometries extracted from each image. The capabilities of this imaging technique are illustrated by various simulated and experimental results carried out in a water tank. Significant improvements in surface image quality, leading to accurate surface reconstruction, are shown for vertical vibrations with displacements of more than two operating wavelengths and for rotational vibrations with deviation angles of less than 1°. The results also show that the resolving power of the ICP algorithm decreases for strong rotational vibrations. [ABSTRACT FROM AUTHOR]

• This study unveils a method to create a substantial PAUT scan database from minimal Full Matrix Capture acquisitions. • This database enables Machine Learning models to generalize effectively, even with limited defect variety. • Integrating geometric context alongside PAUT images significantly boosts detection performance. • The model exhibits robust detection performance, even with noisy images and low CNR, highlighting its potential in real-world applications. Phased array ultrasonic testing (PAUT) requires highly trained and qualified personnel to interpret and analyze images. It takes a solid understanding of wave propagation physics to comprehend the generated images. As such, the inspector's judgment and level of experience have a significant impact on the analysis's outcome. In addition, the procedure is prone to error and laborious. AI had shown to be effective in computer vision in a variety of classification and detection tasks. Regarding PAUT, studies have also demonstrated that machine learning may be able to identify defects with a level of accuracy that is on par or even superior to that of trained and qualified inspectors. Nonetheless, the use of computer vision in PAUT remains very limited. The primary cause of this is the challenge accessing large databases of labelled inspections. In fact, a considerable amount of training data is required for machine learning. While it is easy to access sizeable, labelled databases of MRI scans or photographs for instance, that is not the case in PAUT because inspection results are usually confidential. In this project, a large database was generated using mock-ups commonly used to train and evaluate inspectors. The different defects contained in these mock-ups were used to train a machine learning model. The data was acquired with several different probes centered at different frequencies. Each acquisition was performed using Full Matrix Capture (FMC). The post-processing of the data contained in the FMC allows to compute any sectoral scan from its focal laws. As a result, a comprehensive database composed of hundreds of thousands of sectoral scans was generated from these few FMC acquisitions. The completeness of this database facilitated robust training of a defect detection model for PAUT sectoral scans. The evaluation of the model demonstrated its ability to generalize even to defect types it had never been trained on. Furthermore, the detection performance remained consistent even in high noise conditions where the Contrast-to-Noise Ratio (CNR) was very low. [ABSTRACT FROM AUTHOR]

Repeatable measurements with detachable transducers such as phased arrays have much potential for enhancing the sensitivity of nondestructive evaluation (NDE), as baseline subtraction can be employed. This paper aims to develop post-processing methods to overcome the variation between inspections which leads to poor subtraction performance. In this paper, ultrasonic immersion inspections are performed on a brass sample and a steel sample with a corroded backwall. Reference measurements are taken, and then small defects are machined to simulate defect growth in industrial applications. Defect types include two 1 mm diameter flat bottom holes (FBH) extending from 15 mm to 17 mm in the brass sample and two 2 mm diameter holes drilled to 1 mm depth from the corroded surface of the steel sample to represent the growth of localised pits. Although FMC/TFM is used in this paper, the defects have similar response amplitudes to the structural noise, and they cannot be detected or characterised directly from the images by the conventional 6 dB drop method. This paper demonstrates the utilisation of ultrasonic measurements, together with cross-correlation, to obtain inspection variables which include array position and couplant wave velocity. By knowing the variations between inspection conditions, compensation is made in post-processing to improve image repeatability, and hence the performance of baseline subtraction. The SNR is improved by approximately 10–25 dB depending on how great the actual variations are. It is shown that these previously undetectable defects can be seen and accurately sized after conducting baseline subtraction with compensation. [ABSTRACT FROM AUTHOR]

The phased-array-based ultrasonic full-matrix imaging of carbon fibre-reinforced polymer (CFRP) laminates with total focusing method (TFM) is promising yet challenging due to the layered configuration and strong anisotropy. Traditionally, the precise a priori knowledge of ultrasonic ray paths in anisotropic CFRP laminates are required for accurate TFM imaging, making the inspection process complicated and time consuming. In this work, an adaptive ultrasonic-imaging method is proposed for arbitrarily anisotropic CFRP laminates through full-matrix capture (FMC) and TFM. Firstly, the elastic constants are derived from the FMC data, upon which the 3D group-velocity profiles of ultrasonic waves in CFRP laminates with arbitrary stacking sequences can be calculated. The anisotropic effect is then adaptively corrected for TFM. To verify the effectiveness of proposed method, simulations and experiments are conducted on different anisotropic CFRP laminates with mimicked defects, such as delamination, cracks, voids, etc. The simulated and experimental results well agree with each other, indicating that the proposed method can accurately reconstruct the internal defects of different types. This work provides a convenient and comprehensive avenue towards the ultrasonic inspection of arbitrarily anisotropic CFRP laminates, which markedly benefit the quality control of CFRP components in various fields. [ABSTRACT FROM AUTHOR]

• We proposed a method for monitoring cavitation effects in tissues based on ultrasound images. • We proposed a cavitation discrimination method based on SVM, which can quickly and accurately determine whether cavitation has occurred. • We proposed an improved watershed algorithm to achieve the extraction of cavitation regions. • We proposed a total focusing method based on sparse elements for reconstructing the extracted cavitation region images, effectively improving imaging efficiency while ensuring reconstruction accuracy. • We investigated the developmental patterns of cavitation effects under HIFU. The cavitation effect plays a key role in the field of High-intensity focused ultrasound treatment. By monitoring cavitation to adjust the irradiation dose, effective control of cavitation effects can be achieved, which is crucial for ensuring the safety and efficacy of HIFU therapy. In this paper, a method for monitoring cavitation effects in tissues based on ultrasound images is proposed. First, support vector machine is used to rapidly and accurately determine whether cavitation has occurred. Then, the improved watershed algorithm is employed to extract the regions where cavitation occurs, and the extracted cavitation region images are reconstructed using the total focusing method based on sparse array elements. Finally, to validate the feasibility and stability of the proposed method and investigate the development patterns of cavitation effects. An experimental system was established to conduct cavitation effect monitoring experiments on fresh bovine liver and a simulated human tissue model. The experimental results showed that, except for the recall rate in the fresh bovine liver test set, all classification metrics are greater than 0.9, demonstrating the practicality of the model established in this paper. The improved watershed algorithm achieved average values of Dice Similarity Coefficient, accuracy and precision at 93.69%, 96.74% and 92.23% respectively, representing a significant improvement compared to the traditional watershed method. The proposed total focusing method based on sparse array elements can effectively improve imaging efficiency while ensuring reconstruction accuracy. [ABSTRACT FROM AUTHOR]

Defect detection in non-destructive testing relies on the ability to detect signals arising from defects and distinguish them from those arising from noise. Ultrasonic arrays are capable of producing multiple views of a component through combinations of mode conversions and reflections of waves from boundaries, which potentially contain additional information about defects. Noise in these views can come from random sources such as electrical noise and thermal fluctuation, and coherent sources including stochastic microstructural noise, and boundary reflections giving rise to geometrical artefacts. The number of geometrical artefacts increases with both the geometrical complexity of the part under test, and with the number of reflections used to construct a view. This paper tackles the challenge of defect detection in the presence of geometrical artefacts by characterising the intensity distribution at each point in every view produced within a pristine sample, and using a hypothesis test as a means of defect detection. This information is combined with state-of-the-art data fusion routines, improving detection performance. Experimental work shows that this method improves performance compared to other methods of defect detection over the same scan area, which assume that the defect does not occupy the same location as an artefact in at least one view. Provided the pristine sample is characterised sufficiently well, hypothesis testing provides a robust method of defect detection in the presence of geometrical artefacts. [ABSTRACT FROM AUTHOR]

The total focusing method (TFM) is often considered to be the 'gold standard' for ultrasonic imaging in the field of nondestructive testing. The use of matrix phased arrays as probes allows for high-resolution volumetric TFM imaging. Conventional TFM imaging involves the use of full matrix capture (FMC) for ultrasonic signals acquisition, but in the case of a matrix phased array, this approach is associated with a huge volume of data to be acquired and processed. This severely limits the frame rate of volumetric imaging with 2D probes and necessitates the use of high-end equipment. Thus, the aim of this research was to develop a novel design method for determining the optimal sparse 2D probe configuration for specific conditions of ultrasonic imaging. The developed approach is based on simulated annealing and involves implementing the solution of the sparse matrix phased array layout optimization problem. In order to implement simulated annealing for the aforementioned task, its parameters were set, the acceptance function was introduced, and the approaches were proposed to compute beam directivity diagrams of sparse matrix phased arrays in TFM imaging. Experimental studies have shown that the proposed approach provides high-quality volumetric imaging with a decrease in data volume of up to 84% compared to that obtained using the FMC data acquisition method.