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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]

Total focusing method (TFM) for ultrasonic phased array imaging has the advantages of high imaging resolution, full-scale dynamic focusing and low undetected error rate in ultrasonic detection. However, the problem of long imaging time usually limits its practically applications. The imaging speed can be raised sharply by directly upgrading the detection hardware, but this solution is unavoidable to increase the detection cost. This study is interested in how to improve the TFM imaging speed based on the existing hardware. Thus, an accelerating algorithm combining the asymmetrical optimization mechanism for the full matrix data of ultrasonic array with the sparse one was proposed to simplify the imaging process aiming at reducing the imaging time. Furthermore, TFM imaging experiments of the transverse through hole flaw were finally carried out to evaluate the performance of the proposed algorithm. The results show that in sparse optimization algorithms, the imaging speed of 2 elements sparse is faster and the signal-to-noise ratio of image is higher. The asymmetric optimization and 2 elements sparse optimization are combined in proposed algorithm. The imaging speed of proposed algorithm is improved by 34.56%. Similarly, it is 10.50 and 24.94%, respectively faster than using the asymmetric and 2 elements sparse optimization algorithm. Meanwhile, it is also founded that the signal-to-noise ratio (SNR) of TFM imaging after amplitude optimization is greatly increased by 55.99% without losing the information on the defect characterization. The proposed accelerating algorithm provides an important reference for the practical application of TFM in the ultrasonic phased array flaw detection system. [ABSTRACT FROM AUTHOR]

In-process inspection of the additive manufacturing process requires a technique that can provide reliable measurements given the extreme operating environments, the small size of the defects and the cyclic melting and heating of the material, caused by subsequently deposited layers. A remote and couplant-free ultrasonic inspection technique using bulk waves that can image near-surface defects could address these in-process inspection requirements. Laser induced phased arrays (LIPA) generate and detect ultrasound based on laser ultrasonics principles, while the array is synthesised in post-processing. However, when using LIPAs for inspection, the surface acoustic waves (SAWs) interfere with the bulk wave modes giving rise to crosstalk and artefacts, which makes near-surface defect imaging difficult. This work experimentally validates and compares five techniques for SAW suppression: amplitude thresholding, mean waveform subtraction, principal component subtraction, frequency-wavenumber filtering, and phase coherence imaging. SAW suppression is demonstrated in ultrasonic images of transverse waves based on 71-element LIPA data synthesised on a Ti-6Al-4V directed energy deposition-arc (DED-Arc/Ti6Al4V) sample with a ∼1 mm diameter side drilled hole, located at ∼4 mm below the inspected surface. The reported results show that the principal component subtraction approach achieved the highest ‘signal-to-crosstalk ratio’ improvement of 16 dB, while successfully suppressing the SAW.

Article Highlights This study uses neural networks to probabilistically segment TFM images and investigate uncertainty. The probabilistic U-Net outperforms other models, and high uncertainty is found in the shadow zone, reflections, and small defects. Three different defect types are investigated.

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.

Traditional ultrasonic guided wave imaging detection methods are difficult to accurately characterize structural damage details. In order to obtain detailed features of the damage, the deconvolutional neural network model via deep learning is proposed to investigate the super-resolution imaging problem of subwavelength debonding defects in laminate plates. Initial imaging results are obtained by finite element simulation with the total focusing method. The labeled 12 550 damage images are trained and tested based on extended database expanded by data enhancement method. The results show that compared with the original full-focus imaging algorithm, the deconvolution neural network model improves the accuracy of damage location by 5%, the imaging accuracy is higher than 91%, and the positioning error is lower than 1.8 mm, indicating that the proposed method can significantly improve the resolution of network imaging results and better display the details of subwavelength damage. The above results show that the proposed method has high detection efficiency and does not require manual experience, and has good application value in engineering practice. [ABSTRACT FROM AUTHOR]

Currently, phased arrays are increasingly used in ultrasonic nondestructive testing. One of the most important parameters of ultrasonic nondestructive testing with the application of phased arrays is the angular resolution. This paper presents the results of studies of the angular resolution of concave and convex acoustic arrays in ultrasonic testing with the application of the total focusing method. Computer modeling of concave and convex acoustic arrays consisting of 16, 32 and 64 elements with distances between elements of 0.5 and 1 mm and arc radii of 30 and 60 mm have been performed. The results obtained by computer modeling were confirmed via in situ experiments. [ABSTRACT FROM AUTHOR]

The basic principle of ultrasound is to relate the time of flight of a received echo to the location of a reflector, assuming a known and constant velocity of sound. This assumption breaks down in austenitic welds, in which a microstructure with large oriented austenitic grains induces local velocity differences resulting in deviations of the ultrasonic beam. The inspection problem is further complicated by scattering at grain boundaries, which introduces structural noise and attenuation. Embedding material information into imaging algorithms usually improves image quality and aids interpretation. Imaging algorithms can take the weld structure into account if it is known. The usual way to obtain such information is by metallurgical analysis of slices of a representative mock-up fabricated using the same materials and welding procedures as in the actual component. A non-destructive alternative to predict the weld structure is based on the record of the welding procedure, using either phenomenological models or the finite element method. The latter requires detailed modelling of the welding process to capture the weld pool and the microstructure formation. Several parameters are at play, and uncertainties intrinsically affect the process owing to the limited information available. This paper reports a case study aiming to determine the most critical parameters and levels of complexity of the weld formation models from the perspective of ultrasonic imaging. By combining state-of-the-art welding simulation with time-domain finite element prediction of ultrasound in complex welds, we assess the impact of the modelling choices on the offset and spatial spreading of defect signatures. The novelty of this work is in linking welding simulation with ultrasonic imaging and quantifying the effect of the common assumptions in solidification modelling from the non-destructive examination perspective. Both aspects have not been explored in the literature to date since solidification modelling has not been used to support ultrasonic inspection extensively. The results suggest that capturing electrode tilt, welding power, and weld path correctly is less significant. Bead shape was identified as having the greatest influence on delay laws used to compute ultrasonic images. Most importantly, we show that neglecting mechanical deformation in FE, allowing for simpler thermal simulation supplemented with a phenomenological grain growth loop, does not reduce the quality of the images considerably. Our results offer a pragmatic balance between the complexity of the model and the quality of ultrasonic images and suggest a perspective on how weld formation modelling may serve inspections and guide pragmatic implementation. [ABSTRACT FROM AUTHOR]

As the demand of structural integrity in manufacturing industries is increasing, the ultrasonic array technique has drawn more attention thanks to its inspection flexibility and versatility. By taking advantage of the possibility of individual triggering of each array element, full matrix capture (FMC) data acquisition strategy has been developed that contains the entire information of an inspection scenario. Total focusing method (TFM) as one of the ultrasonic imaging algorithms, is preferably applied to FMC dataset since it uses all information in FMC to synthetically focus the sound energy at every image pixel in the region of interest. Half-skip TFM (HSTFM) is proposed in multi-mode TFM imaging that involves a backwall reflection wave path, so that the defect profile could be reconstructed for accurate defect characterization. In this paper, a method involving Snell's law-based wave mode conversion is proposed to account for more reasonable wave propagation time when wave mode conversion happens at backwall reflection in HSTFM. A series of model based simulations (in software simSUNDT) are performed for parametric studies, with the intention of investigating the capability of defect characterization using HSTFM with varying tilt angle and relative position of surface-breaking notch to array probe. The results show that certain TFM modes could help with defect characterization, but the effectiveness is limited with varying defect features. It is inappropriate to address a certain mode for all characterization perspectives but rather a combination, i.e., multi-mode TFM, should be adopted for possible interpretation and characterization of defect features. [ABSTRACT FROM AUTHOR]

In the article, the study of the quality of tomogram focusing during the inspection of objects with curved surfaces by flexible acoustic array was described. The main goal of the study was theoretically and experimentally define the acceptable deviation limits of the elements' coordinates values. The tomogram reconstruction was performed by the total focusing method. The Strehl ratio was chosen as a criterion for assessing the quality of tomogram focusing. The ultrasonic inspection procedure were simulated and validated experimentally by means of convex and concave curved arrays. In the study, it was proven that the elements coordinates of the flexible acoustic array were determined with an error of no more than 0.18λ and the tomogram image was obtained in sharp focus. [ABSTRACT FROM AUTHOR]

Guided acoustic waves (GAW) have proven to be a useful tool for structural health monitoring (SHM). However, the dispersive nature of commonly used Lamb waves compromises the spatial resolution making it difficult to detect small or weakly reflective defects. Here we demonstrate an approach that can compensate for the dispersive effects, allowing advanced algorithms to be used with significantly higher signal-to-noise ratio and spatial resolution. In this paper, the sign coherence factor (SCF) extension of the total focusing method (TFM) algorithm is used. The effectiveness is examined by numerical simulation and experimentally demonstrated by detecting weakly reflective layers with a highly dispersive A0 mode on an aluminum plate, which are not detectable without compensating for the dispersion effects. [ABSTRACT FROM AUTHOR]

Rayleigh waves can be used to detect surface defects effectively. However, the Rayleigh wave imaging quality is often poor due to the low reflected energy when using a single probe. To accomplish high-resolution imaging of surface defects, we propose a total focusing method with Rayleigh waves (TFMRW) combined with principal component analysis (PCA), in an approach called TFMRW-PCA. Firstly, the propagation characteristics of Rayleigh waves are analysed using simulation, and full matrix capture (FMC) data are obtained. Secondly, we use the Fermat principle to calculate the time of flight of the ultrasonic waves, and an improved TFM (TFMRW) algorithm is established to post-process the FMC data. Finally, PCA is used to separate the interference wave from the signal after processing by the TFMRW algorithm. In this paper, the effect of the quantity of elements and the location of the defect on the imaging results are analysed through simulation and experiment. The results show that TFMRW can accurately characterise the surface defects in the sample, with an average defect size error of 0.14 mm2. Moreover, when combined with PCA, the average API value is reduced by 0.06 and the average signal-to-noise ratio (SNR) is increased by 6.26 dB. [ABSTRACT FROM AUTHOR]

The development of phased array ultrasonic testing (PAUT) using the total focusing method (TFM) has led to improved quality in the detection and characterization of defects that may occur in device components. The transducer is usually coupled to the specimen via the plexiglass wedge to allow for the transfer of energy into target areas when using PAUT with full matrix capture (FMC). Due to the vast number of transmission delay sequences and the calculation power necessary to receive the focus for ultrasonic inspection using PAUT with FMC, a time-efficient technique for finding the incidence point is critical. This paper describes a segmental search method (SSM) for quickly determining the point of incidence of a wedge dual-layer medium. The search interval was narrowed by imposing the constraint that the point of incidence should be between the source and the target. In addition, finding the appropriate step size for the search range minimized the time spent on search processing. This method can be computationally more efficient than the analytical method (ANM) and Newton iteration method (NIM) used to solve Snell's law and can ensure beam accuracy. [ABSTRACT FROM AUTHOR]

The feasibility of using laser-generated ultrasonic Total Focusing Method (TFM) was assessed for guided ultrasonic waves in finite plates. The application under consideration is for inspection of ultrasonically welded battery tab-to-electrode foil stack joints. The testing constraints for this weld necessitate couplant-free, remote, guided-wave conditions making laser ultrasonic TFM an ideal inspection technique. It was determined that laser-generated guided wave TFM can be used to remotely assess defects in a finite plate when the defects are strong reflectors in the plane of wave propagation. The finite dimensions of the tab require a strong understanding of the edge reflection effects on the TFM image. The guided wave modes used in this study were strongly affected by scattering due to the complex weld geometry, which most resembles that of a periodic triangular grated wave guide. Future work will investigate methods to compensate for the strong scattering/guided wave effects, the use of other guided wave geometries, out of plane TFM reconstruction for other weld defect types, as well as apodization effects. [ABSTRACT FROM AUTHOR]

Based on a synthesized laser ultrasonic array, full matrix capture can be used to acquire data, which can then be post-processed using the total focusing method. However, this noncontact ultrasonic imaging technique has not been widely used because of the numerous artifacts in ultrasonic images and time-consuming data acquisition. To address these issues, this study proposes a post-processing algorithm, which uses the laser ultrasound directivity information to suppress the artifacts in the total focusing method’s images. In particular, a weight factor is defined using the directivity information. By multiplying the image intensity of the total focusing method with this factor, the algorithm uses not only the amplitude and phase information of laser ultrasound but also its directivity information. The experimental results indicate that four types of artifacts are suppressed. Because the grating lobe artifacts can be suppressed, a larger element spacing can be used to reduce the data acquisition time.

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.

Immersion ultrasonic phased array imaging technology offers great advantages, particularly in coupling and automatic detection of industrial non-destructive testing (NDT). To suppress the influence of secondary interface echoes in the immersion ultrasonic phased array imaging, a novel phase circular statistics vector (PCSV) weighting method is proposed in this paper. Firstly, the PCSV factor matrix is established according to the phase consistency of the echo signals. Secondly, due to the higher phase coherence of the defect echo, the PCSV factor matrix is used to weight the TFM image to suppress the secondary interface echo. The result shows the secondary interface echoes are effectively suppressed in the total focusing method (TFM) image on a 0~40 dB scale. It is also shown that PCSV weighting could not only suppress the secondary interface echoes but also improved the image quality in terms of SNR and lateral resolution by comparing with traditional TFM. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced polymers (CFRP) are extensively used in aerospace applications. Out-of-plane wrinkles frequently occur in aerospace CFRP parts that are commonly large and complex. Wrinkles acting as failure initiators severely damage the mechanical performance of CFRP parts. Wrinkles have no significant acoustic impedance mismatch, reflecting weak echoes. The total focusing method (TFM) using weak reflection signals is vulnerable to noise, so our primary work is to design discrete-time filters to relieve the noise interference. Wrinkles in CFRP composites are geometric defects, and their direct detection requires high spatial precision. The TFM method is a time-domain delay-and-sum algorithm, and it requires that the time information of filtered signals has no change or can be corrected. A linear phase filter can avoid phase distortion, and its filtered signal can be corrected by shifting a constant time. We first propose a wrinkle detection method using linear phase FIR-filtered ultrasonic array data. Linear phase filters almost do not affect the wrinkle geometry of detection results and can relieve noise-induced dislocation. Four filters with different bandwidths have been designed and applied for wrinkle detection. The 2 MHz bandwidth filter is recommended as an optimum choice. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tiangong University is the property of Journal of Tianjin Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The defects in the welds of energy pipelines have significantly influenced their safe operation. The inefficient and inaccurate detection of the defects may give rise to catastrophic accidents. Ultrasonic phased array inspection is an important means of ensuring pipeline safety. The total focusing method (TFM), using ultrasonic phased arrays, has become widely used in recent years in non-destructive evaluation (NDE). However, manual defect recognition of TFM images is seen to lack accuracy and robustness, arising from deficiency of practical experience. In this paper, the automated classification of different defects from TFM images is studied with a view to facilitate inspection efficacy. By experimentally implementing the TFM approach on a bespoke specimen, the images corresponding to crack-like defects and pore-like defects were employed to investigate the effectiveness of four different machine learning models (known as Support Vector Machine, CART Decision tree, K Nearest Neighbors, Naive Bayes) containing data augmentation, feature extraction and defect classification. The results suggested that the accuracy of defect classification using the HOG-Poly-SVM algorithm was 93%, which outperformed the results from other algorithms. The advantage of the HOG-Poly-SVM algorithm used in defect classification of ultrasonic phased array TFM data is discussed by conducting ten-fold cross validation and other evaluation metrics. In this paper, in order to improve the efficiency of detecting pipeline defects in the future, and for testing test blocks simulating buried pipelines containing defects, we proposed, for the first time, that ultrasonic phased-array TFM imaging results in small object detection images, and found that the SVM algorithm was applicable to ultrasonic phased array TFM imaging, providing a research method and ideas for the use of artificial intelligence in industrial non-destructive testing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The basic principle of ultrasound is to relate the time of flight of a received echo to the location of a reflector, assuming a known and constant velocity of sound. This assumption breaks down in austenitic welds, in which a microstructure with large oriented austenitic grains induces local velocity differences resulting in deviations of the ultrasonic beam. The inspection problem is further complicated by scattering at grain boundaries, which introduces structural noise and attenuation. Embedding material information into imaging algorithms usually improves image quality and aids interpretation. Imaging algorithms can take the weld structure into account if it is known. The usual way to obtain such information is by metallurgical analysis of slices of a representative mock-up fabricated using the same materials and welding procedures as in the actual component. A non-destructive alternative to predict the weld structure is based on the record of the welding procedure, using either phenomenological models or the finite element method. The latter requires detailed modelling of the welding process to capture the weld pool and the microstructure formation. Several parameters are at play, and uncertainties intrinsically affect the process owing to the limited information available. This paper reports a case study aiming to determine the most critical parameters and levels of complexity of the weld formation models from the perspective of ultrasonic imaging. By combining state-of-the-art welding simulation with time-domain finite element prediction of ultrasound in complex welds, we assess the impact of the modelling choices on the offset and spatial spreading of defect signatures. The novelty of this work is in linking welding simulation with ultrasonic imaging and quantifying the effect of the common assumptions in solidification modelling from the non-destructive examination perspective. Both aspects have not been explored in the literature to date since solidification modelling has not been used to support ultrasonic inspection extensively. The results suggest that capturing electrode tilt, welding power, and weld path correctly is less significant. Bead shape was identified as having the greatest influence on delay laws used to compute ultrasonic images. Most importantly, we show that neglecting mechanical deformation in FE, allowing for simpler thermal simulation supplemented with a phenomenological grain growth loop, does not reduce the quality of the images considerably. Our results offer a pragmatic balance between the complexity of the model and the quality of ultrasonic images and suggest a perspective on how weld formation modelling may serve inspections and guide pragmatic implementation.

An ultrasonic phased array consists of multiple ultrasonic probes arranged in a certain regular order, and the delay time of the excitation signal sent to each array element is controlled electronically. The testing system model based on ultrasonic propagation theory is established to obtain a controllable and focused sound field, which has theoretical and engineering guiding significance for the calculation and analysis of ultrasonic array sound fields. Perfecting array theory and exploring array imaging methods can obtain rich acoustic information, provide more intuitive and reliable research results, and further the development of ultrasonic phased-array systems. This paper reviews the progress of research on the application of ultrasound arrays for non-destructive testing (NDT) and brings together the most relevant published work on the application of simulation methods and popular imaging techniques for ultrasonic arrays. It mainly reviews the modeling approaches, including the angular spectrum method (ASM), multi-Gaussian beam method (MGB), ray tracing method, finite element method (FEM), finite difference method (FDM), and distributed point source method (DPSM), which have been used to assess the performance and inspection modality of a given array. In addition, the array of imaging approaches, including the total focusing method (TFM), compression sensing imaging (CSI), and acoustic nonlinearity imaging (ANI), are discussed. This paper is expected to provide strong technical support in related areas such as ultrasonic array testing theory and imaging methods. [ABSTRACT FROM AUTHOR]

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair. [ABSTRACT FROM AUTHOR]

The T-shaped weld structure of LNG tank nickel-based alloy is complex and the grain of the weld is coarse and anisotropy. The conventional ultrasonic detection technique cannot effectively detect its internal defects. In this paper, the acoustic model of T-shaped weld was built based on CIVA software, the ultrasonic wave propagation characteristics and defect response in 9% Ni alloy specimen was simulated. Then, the 9% Ni alloy specimen with artificial flaws was designed, and the phased array imaging based on total focusing method was studied using double-sided array. The quantification and localization of artificial defects were successfully detected. Finally, the research results were applied to the engineering project, and the lack of penetration flaw in the T-shaped weld of one large LNG storage tank were successfully detected, which effectively ensuring the safe use of the storage tank. [ABSTRACT FROM AUTHOR]

• The virtual source total focusing method is introduced into the leaky Rayleigh wave detection. • The problem of wave attenuation is improved by the virtual source technology. • The coherence factor weighting function is utilized to suppress the noise and artifacts. Leaky Rayleigh waves are sensitive to surface defects and beneficial for automated detection due to their non-contact characteristics. However, the amplitude of the leaky Rayleigh waves is low due to the attenuation of waveform conversion and acoustic waves propagation, which limits the imaging quality for long-distance detection. This study introduces a novel method combining leaky Rayleigh waves detection with virtual source total focusing method. The virtual source (VS) technology enhances the emission energy of leaky Rayleigh waves. Then, the total focusing method (TFM) is applied to acquire high-accuracy images of defects. To reduce noise and artifacts, a weighting function based on the coherence factor (CF) is developed to weight the TFM superimposed signals, thereby achieving high-quality image reconstruction. Experimental results show that compared with the conventional TFM method, the proposed method can effectively improve the amplitude of imaging signals while reducing system noise and imaging artifacts. The lateral accuracy of defects is improved, with the average lateral error of defect size being 0.178 mm. The signal-to-noise ratio (SNR) of ultrasound images is increased by 27.59 dB, and the array performance index (API) of ultrasound images is decreased by 33.78 %. The proposed method provides a new and effective approach for quantitatively assessing the surface defects of metal components using leaky Rayleigh waves. [ABSTRACT FROM AUTHOR]

In this study, we propose a class-conditioned Generative Adversarial Autoencoder (cGAAE) to improve the realism of simulated ultrasonic imaging techniques, in particular the Multi-modal Total Focusing Method (M-TFM), based on the availability of both simulated and experimental TFM images. In particular, this work studied the case of the inspection of a complex geometry block representative of weld-inspection problem based on ultrasonic multi-elements probe. The cGAAE is represented by a tailored learning schema, trained in a semi-supervised fashion on a labeled mixture of synthetic (class 0) and experimental (class 1) M-TFM images, obtained under different meaningful inspection set-ups parameters (i.e., the celerity of the transverse ultrasonic wave, the specimen back-wall slope and height, the flaw tilt and heigh). That is, the cGAAE schema consists in a combination of learning stages involving class-conditioned spatial-transformers and arbitrary style transfer endows the cGAAE of powerful generative features, such as quasi real-time generation of M-TFM images by sweep of the inspection parameters. We exploited the cGAAE model to improve the realism of simulated M-TFM images and enhance the accuracy of the inverse problem, aiming at estimating the inspection parameters based on experimental acquisitions. • A deep learning generative model is conceived for a small non-destructive data set. • State-of-the-art generative networks from large public data set are applied. • Spatial Transformer is adapted for a class-aware generator and discriminator. • A domain-adapted representation is learned and inspected for generation proposes. [ABSTRACT FROM AUTHOR]

In the article, the study of the quality of tomogram focusing during the inspection of objects with curved surfaces by flexible acoustic array was described. The main goal of the study was theoretically and experimentally define the acceptable deviation limits of the elements’ coordinates values. The tomogram reconstruction was performed by the total focusing method. The Strehl ratio was chosen as a criterion for assessing the quality of tomogram focusing. The ultrasonic inspection procedure were simulated and validated experimentally by means of convex and concave curved arrays. In the study, it was proven that the elements coordinates of the flexible acoustic array were determined with an error of no more than 0.18λ and the tomogram image was obtained in sharp focus.

• 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]

Guided acoustic waves (GAW) have proven to be a useful tool for structural health monitoring (SHM). However, the dispersive nature of commonly used Lamb waves compromises the spatial resolution making it difficult to detect small or weakly reflective defects. Here we demonstrate an approach that can compensate for the dispersive effects, allowing advanced algorithms to be used with significantly higher signal-to-noise ratio and spatial resolution. In this paper, the sign coherence factor (SCF) extension of the total focusing method (TFM) algorithm is used. The effectiveness is examined by numerical simulation and experimentally demonstrated by detecting weakly reflective layers with a highly dispersive A0 mode on an aluminum plate, which are not detectable without compensating for the dispersion effects.

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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%. [ABSTRACT FROM AUTHOR]

We present an imaging pipeline to achieve enhanced images of the interior of concrete from ground penetrating radar (GPR) and ultrasonic echo array (UEA) measurements. This work lays the foundation for an advanced yet practical imaging tool to assess concrete structures. Specifically, we propose an enhanced two-dimensional (2D) total focusing method (XTFM) to reconstruct images from raw GPR and UEA data. The proposed XTFM algorithm integrates total focusing method (TFM) and synthetic aperture focusing technique (SAFT) concepts to post-process large independent and interelement measurements from both modalities in a computationally efficient way. Furthermore, we introduce a novel 2D image fusion algorithm using wavelet multilevel decomposition and an NDT knowledge-based rule to fuse GPR and UEA images. We then compare our algorithm with conventional fusion algorithms such as averaging, maximum, and product. The results from three laboratory concrete reference specimens are evaluated in detail. The fused images are compared with each other as well as benchmarked with the original GPR and UEA images. The output image obtained from our proposed pipeline is an enhanced 2D image of the interior of concrete structures that eases interpretation by a human inspector as well as it has the potential to improve interpretation by computer vision and image analysis algorithms. [ABSTRACT FROM AUTHOR]

Ultrasonic phased array technology is used in various fields. Traditional full phased arrays place elements in every position of a uniform lattice with half-wavelength spacing between the lattice points, so the hardware cost is very high. This paper introduces an automatically method to sparsify the full array method with well-controlled sidelobes and the main lobe. By calculating one-dimensional phased array patterns that can reflect phased array performance, the binary particle swarm optimization (BPSO) algorithm is used to optimize the array layout. The method initialized form full array and decreased several elements step by step, then, a sparse array with comprehensive acoustic performance close to the reference full array is obtained. By applying the proposed method to the sparse array design of total focusing method (TFM), the simulation results indicate that the proposed sparse total focusing method can greatly increase computational efficiency while providing significantly higher image quality. The BPSO can provide effective optimization design for sparse arrays.

Carbon fiber-reinforced polymers (CFRP) are extensively used in aerospace applications. Out-of-plane wrinkles frequently occur in aerospace CFRP parts that are commonly large and complex. Wrinkles acting as failure initiators severely damage the mechanical performance of CFRP parts. Wrinkles have no significant acoustic impedance mismatch, reflecting weak echoes. The total focusing method (TFM) using weak reflection signals is vulnerable to noise, so our primary work is to design discrete-time filters to relieve the noise interference. Wrinkles in CFRP composites are geometric defects, and their direct detection requires high spatial precision. The TFM method is a time-domain delay-and-sum algorithm, and it requires that the time information of filtered signals has no change or can be corrected. A linear phase filter can avoid phase distortion, and its filtered signal can be corrected by shifting a constant time. We first propose a wrinkle detection method using linear phase FIR-filtered ultrasonic array data. Linear phase filters almost do not affect the wrinkle geometry of detection results and can relieve noise-induced dislocation. Four filters with different bandwidths have been designed and applied for wrinkle detection. The 2 MHz bandwidth filter is recommended as an optimum choice.

Immersion ultrasonic phased array imaging technology offers great advantages, particularly in coupling and automatic detection of industrial non-destructive testing (NDT). To suppress the influence of secondary interface echoes in the immersion ultrasonic phased array imaging, a novel phase circular statistics vector (PCSV) weighting method is proposed in this paper. Firstly, the PCSV factor matrix is established according to the phase consistency of the echo signals. Secondly, due to the higher phase coherence of the defect echo, the PCSV factor matrix is used to weight the TFM image to suppress the secondary interface echo. The result shows the secondary interface echoes are effectively suppressed in the total focusing method (TFM) image on a 0~40 dB scale. It is also shown that PCSV weighting could not only suppress the secondary interface echoes but also improved the image quality in terms of SNR and lateral resolution by comparing with traditional TFM.

To increase the speed of recording of echo signals and increase the speed of reconstruction of the image of reflectors, it is proposed to use a sparse switching matrix (SMC). To obtain a switching matrix that allows obtaining images that are minimally different from the image obtained using a full switching matrix (FMC), it is proposed to use a genetic algorithm. Two options for optimization of the switching matrix are considered: sparsing elementwise and sparsing by columns. In numerical and model experiments, it is shown that the sparse switching matrix determined using the genetic algorithm, filled 25%, allows the formation of images that differ from the image obtained by FMC with an error of approximately 3%. Working with a switching matrix by columns allows one to increase the speed of recording of echo signals by 4 times. The speed of image reconstruction is increased by the same factor. [ABSTRACT FROM AUTHOR]

Advanced ultrasonic array-based imaging techniques such as the total focusing method (TFM) and plane wave imaging (PWI) have shown an increased efficiency to image smaller defects with high resolution. In this work, a new imaging technique called sectorial plane wave imaging (SPWI) is proposed by employing the principle of PWI to a framework of the conventional phased array sectorial scan. Besides, the SPWI algorithm is extended for multi-modal ultrasonic imaging using a linear array on an angular wedge. Initially, a plane strain finite element (FE) framework is presented to simulate the forward problem of elastic wave propagation excited by a linear array of elements. In this work, a specimen with an inclined notch is modeled, and multiple FE simulations are carried out to compute the full matrix capture array response. Subsequently, experiments are carried out on specimens with defects to demonstrate the proposed SPWI algorithm’s imaging capabilities. TFM, PWI, and SPWI algorithms are applied for image reconstruction, and their numerical and experimental results are compared. The direct and half-skip mode SPWI results are shown to capture all the specimen defects with good accuracy but at a reduced image resolution compared to TFM and PWI results due to a compromise in reception focusing. However, the enhanced processing speed of SPWI compared to TFM and PWI makes it suitable for quick multi-mode inspections and a viable replacement of the widely used conventional sector scan. [ABSTRACT FROM AUTHOR]

The defects in the welds of energy pipelines have significantly influenced their safe operation. The inefficient and inaccurate detection of the defects may give rise to catastrophic accidents. Ultrasonic phased array inspection is an important means of ensuring pipeline safety. The total focusing method (TFM), using ultrasonic phased arrays, has become widely used in recent years in non-destructive evaluation (NDE). However, manual defect recognition of TFM images is seen to lack accuracy and robustness, arising from deficiency of practical experience. In this paper, the automated classification of different defects from TFM images is studied with a view to facilitate inspection efficacy. By experimentally implementing the TFM approach on a bespoke specimen, the images corresponding to crack-like defects and pore-like defects were employed to investigate the effectiveness of four different machine learning models (known as Support Vector Machine, CART Decision tree, K Nearest Neighbors, Naive Bayes) containing data augmentation, feature extraction and defect classification. The results suggested that the accuracy of defect classification using the HOG-Poly-SVM algorithm was 93%, which outperformed the results from other algorithms. The advantage of the HOG-Poly-SVM algorithm used in defect classification of ultrasonic phased array TFM data is discussed by conducting ten-fold cross validation and other evaluation metrics. In this paper, in order to improve the efficiency of detecting pipeline defects in the future, and for testing test blocks simulating buried pipelines containing defects, we proposed, for the first time, that ultrasonic phased-array TFM imaging results in small object detection images, and found that the SVM algorithm was applicable to ultrasonic phased array TFM imaging, providing a research method and ideas for the use of artificial intelligence in industrial non-destructive testing.