Phased Array Ultrasonic Testing Research Articles

Distributed Backface Strain Sensing of Composite Adhesively Bonded Joints under Mode II Fatigue Loading

When investigating the fracture behaviour of adhesive joints, accurate crack tip localization is under mode II loading is particularly challenging due to the absence of crack opening. The crack front often has a curved shape, so methods that inspect the side of the specimen cannot capture the crack length throughout its width. In this study, distributed backface strain sensing using Optical Backscatter Reflectometry fibre sensors is used to monitor fatigue crack growth in adhesively bonded composite End Notched Flexure Specimens. The obtained backface strain profiles were compared with analytical and Finite Element solutions to understand their relationship with the crack length. Results show that the crack tip position can be found by a local extremum in the first derivative of the strain profile. The proposed monitoring technique was compared with more established techniques for crack length measurement, such as Visual Testing, Compliance Monitoring, and Phased Array Ultrasonic Testing, and was shown to provide good accuracy. The evaluation of these techniques not only plays a crucial role in ensuring accurate testing, but can also contribute to the development of effective strategies for inspection and monitoring of adhesive joints.

Round robin experiment to detect, size, and characterize flaws in the welds of existing hydraulic steel structures using phased array ultrasonic testing

Limited information exists on the ability of nondestructive testing techniques to detect, size, and characterize flaws in existing hydraulic steel structures (HSS). Round robin experiments were conducted using phased array ultrasonics to inspect welded steel specimens representing joints in existing HSS. Technicians detected 83% of the flaws scanned, but detection rates varied widely by flaw and technician. Uncertainty in flaw size estimates, represented by 90% confidence bounds on the ratio of estimated to actual length or height, ranged from 0.52 to 2.10 for length and 0.32 to 3.59 for height. Planar, volumetric and laminar flaws were accurately characterized 80% of the time.

LNG storage tanks 9% Ni steel weld inspection via phased-coherent TFM and SVD enhancement

ABSTRACT 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.

Phased Array Ultrasonic Inspection of Composite Fuselage Panel

Abstract The composite fuselage panel is a crucial component in large civil aircraft, necessitating higher detection rates and efficiency. Phased array ultrasonic testing can enhance the efficiency and defect detection rate; however, it is essential to consider the structural characteristics of the fuselage to ensure reliable identification of design defects. Based on the material properties, structural features, and defect detection requirements of composite fuselage panels, a test block reflecting their characteristics was designed and fabricated with various prefabricated defects. Ultrasonic phased array method was employed for a series of testing parameter optimization experiments and defect detectability analysis. The detected flaws were quantitatively analyzed and compared with those obtained using conventional ultrasonic automatic scanning techniques to validate the effectiveness of the developed ultrasonic phased array method. The feasibility of utilizing phased array ultrasonics for nondestructive evaluation of wide-body airliner composite fuselage panels is confirmed.

Signal de-noising based on ICEEMDAN for delamination detection by phased array ultrasonic testing

Abstract Aiming at the problem of clutter noise of inter-layer reflections that occurs in multilayer heterogeneous medium such as hybrid fibre composites during ultrasonic inspection, a signal de-noising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed in this paper. In the method, the cross-correlation coefficient is utilized to identify the intrinsic mode function (IMF) that is related to raw ultrasonic signal. Particularly, the root mean square error (RMSE), as selection criterions, are employed conjunctively to sift the optimal IMF among selected decomposition signals. Hilbert transform is used to extract the envelope of the signal after removing the interlayer reflection noise. Experimental results show that, the proposed method can adaptively decompose the non-stationary ultrasonic signal into several IMFs with different spectrum so as to dealing with clutter noises. For delaminate detection by phased array ultrasonic testing for hybrid fibre composites, structural noises are significantly suppressed in the reconstructed signal that retains the distinct echo characteristics of a delaminated defect.

Development of Phased Array Ultrasonic Testing System Using Distributed Architecture

Abstract With more rigorous requirements for reliability and safety of structures and materials, high resolution and fast imaging have been urgent needs of Phased Array Ultrasonic Testing (PAUT). In order to improve the detection resolution and efficiency of complex structures and materials such as ultra-thick carbon steel and honeycomb plate, a PAUT system is proposed in this paper. The system uses a distributed architecture, which not only divides hardware resources, improves system performance, but also is scalable. PA, single- and multi-channel conventional UT, and TOFD (Time Of Flight Diffraction) modalities are combined, and PA modality is carried by the fully parallel non-multiplexed 128 PE (Pulse Echo) electronics with independently adjustable emitting and receiving parameters. In addition, the ultrasound analog front-end circuit is designed using discrete devices, providing more powerful analog performance than integrated chips. GTX (Gigabit Transceiver) and PCIe (Peripheral Component Interconnect Express) are applied to high-speed data transmission, providing raw ultrasound data. Characterization and verification results indicate that the system is ideal for challenging inspections in many industries.

Automatic PAUT crack detection and depth identification framework based on inspection robot and deep learning method

Orthotropic steel bridge decks (OSD) are widely acclaimed for their lightweight, high load-carrying capacity, and adaptability, making them a popular choice in steel structure bridges. However, the complex nature of their structure makes them susceptible to fatigue cracking, posing significant safety concerns. To address the issues above, this study employs a robot equipped with an ultrasonic phased array probe to automate the detection of internal cracks within Orthotropic Steel Decks (OSD). A Deep Convolutional Generative Adversarial Network (DCGAN) is utilized to augment the training dataset of Phased Array Ultrasonic Testing (PAUT) images. The YOLO series algorithms are applied and compared for crack localization, with YOLO v7-tiny exhibiting the highest accuracy and speed. Integrating attention mechanisms into the YOLO v7-tiny algorithm to facilliate rapid and high-precision crack detection. Analyzing the echo region with an echo intensity bar enabled the identification of crack depth, with an identification error within 5%.

Mitigating RGB-D camera errors for robust ultrasonic inspections using a force-torque sensor

ABSTRACT Robot-based phased array ultrasonic testing is widely used for precise defect detection, particularly in complex geometries and various materials. Compact robots with miniature arms can inspect constrained areas, but payload limitations restrict sensor choice. RGB-D cameras, due to their small size and light weight, capture RGB colour and depth data, creating colourised 3D point clouds for scene representation. These point clouds help estimate surface normals to align the ultrasound transducer on complex surfaces. However, sole reliance on RGB-D cameras can lead to inaccuracies, affecting ultrasonic beam direction and test results. This paper investigates the impact of transducer pose and RGB-D camera limitations on ultrasonic inspections and proposes a novel method using force-torque sensors to mitigate errors from inaccurately estimated normals from the camera. The force-torque sensor, integrated into the robot end effector, provides tactile feedback to the controller, enabling joint angle adjustments to correct errors in the estimated normal. Experimental results show the successful application of ultrasound transducers using this method, even with significant misalignment. Adjustments took approximately 4 seconds to correct deviations from 12.55°, with an additional 4 seconds to ensure the probe was parallel to the surface, enhancing ultrasonic inspection accuracy in complex, constrained environments.

DFW-YOLO: YOLOv5-based algorithm using phased array ultrasonic testing for weld defect recognition

ABSTRACT Although phased array ultrasonic testing (PAUT) technology has been widely employed in pipeline weld defect recognition, current defect recognition still relies on manual operation, which limits its efficiency. By combining deep learning with PAUT, this paper presents a new neural architecture based on YOLOv5 called DFW-YOLO that can call defect indications automatically. To address redundant defect feature maps, the FasterNet backbone is introduced to enhance the model’s feature extraction ability. Additionally, partial dilated convolution (PDConv) and partial convolution (PConv) with an atrous convolution branch are designed to extend the receptive field of the backbone network, mitigating the chance of feature loss. Furthermore, a weighted generalised feature pyramid network (WGFPN) structure is proposed as the neck network. This structure combines the reparameterized generalised feature pyramid network (RepGFPN) with the weighted aggregated concatenation (WAC) structure and eliminates inefficient branches to enhance the model’s feature fusion ability. The experimental results demonstrated that DFW-YOLO outperforms the original YOLOv5 in terms of detection performance. The precision, recall, mean average precision (mAP)@0.5, and mAP@0.5:0.95 are indicators for DFW-YOLO and reach 97.4%, 97.5%, 98.3%, and 68.2%, respectively, marking 4.8%, 5%, 2.7%, and 22.5% improvement compared to the original YOLOv5.

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 Complementary Fusion-Based Multimodal Non-Destructive Testing and Evaluation Using Phased-Array Ultrasonic and Pulsed Thermography on a Composite Structure.

Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis.

Inspection of wind turbine bolted connections using the ultrasonic phased array system

This study explores the inspection of bolted connections in wind turbines, specifically focusing on the application of Phased Array Ultrasonic Testing (PAUT). The research comprises four sections: Acoustoelastic Constant calibration, high tension investigation on bolts, blind tests on larger bolts, and Finite Element Analysis (FEA) verification. The methodology shows accurate results for stress while the bolt is under operative loads, and produces a clear indication of when it is above these loads and beginning to deform. PAUT emerges as a promising tool for bolt inspection, offering multiple imaging modes for simultaneous stress monitoring and defect detection. The study advocates for PAUT as a robust method to enhance wind turbine safety, longevity, and future in-situ testing.

Guided Wave Phased Array Ultrasonic Imaging for Thin Plate Inspection

This study focuses on the application of phased array ultrasonic testing with guided waves for Structural Health Monitoring (SHM) in thin plates. Addressing the limitations of conventional ultrasonic testing efficiently by inspecting thin, long structures. This study employs phased array technology to thin isotropic plates at high frequency(2MHz), thereby elevating imaging and defect detection in structural components. Through the utilization of the full matrix capture (FMC) scanning approach in 3-D finite element (FE) simulations, A0 and S0 modes were generated on steel plates with through-hole geometries. The process involved capturing reflected signals to form an FMC matrix that includes all A-scan signals. Subsequently, the total focusing method (TFM) algorithm was applied to FMC data for the reconstruction of focused images of A0 and S0 modes separately. A key improvement in our approach is integrating a time-domain filtering method into the FMC data. This enhancement addresses the dispersive nature of guided waves, thus improving the accuracy in identifying defect locations and sizes within the images. Our finite element simulations on steel specimens, with varied through-hole locations, revealed the method's potential in accurately imaging defects located deeper within the structure. Additionally, the study expanded to evaluate the efficacy of phased array ultrasonic testing (PAUT) in SHM, particularly in conjunction with guided waves to study the flaw detection sensitivity of the defect through the TFM algorithm on isotropic steel plates up to wavelength(λ)/16. This whole analysis considered crucial factors such as ultrasonic wave mode, frequency, and group velocity for the structure under inspection. This study represents a significant contribution to the field of SHM, introducing a sophisticated, real-time monitoring methodology that promises to enhance structural integrity assessment. Keywords: SHM, PAUT, GW, FMC, TFM FE Simulations, Time Domain Filtering, Ultrasonic Testing.

Crack Tip Localization in Adhesively Bonded CFRP-CFRP Joints subjected to Mode II Fatigue Loading

The use of resin-based composite materials in place of metals has gained prominence across various industries. Such composites offer a superior strength-to-weight ratio, addressing the increasing demand for lightweight structures to reduce emissions. As such, structural joining of composite materials is of growing importance. Traditional fasteners, however, not only compromise weight savings, but also introduce weak points within the structure. Adhesive bonding emerges as the most promising joining technology, balancing weight efficiency and performance. However, its broader adoption for primary structural joints is hindered by a lack of standardization of quality control and non-destructive inspection techniques and concerns about long-term reliability. To address this challenge, the “CERTBOND” COST Action initiative (CA18120) was established to investigate adhesive bonding and develop a reliable certification roadmap for bonded primary structures. As part of this initiative, a Mode II fatigue loading Round Robin study was conducted on adhesively bonded CFRP-CFRP End-Notched Flexure specimens. While conducting the fatigue tests, supplementary techniques for estimating the crack tip position were employed. The selected techniques included visual testing (employing two different methods), ultrasonic phased array testing, distributed strain sensing using optical backscatter reflectometry, digital image correlation, and localization by acoustic emission. The present study describes, compares and discusses the results obtained by the cited localization techniques.

Phased Array Ultrasonic Imaging for Characterizing Steel with an Elevated Temperature Gradient.

In this research work a phased array ultrasonic Testing (PAUT) is used for inspecting steel at elevated temperature. Velocity of ultrasound is affected by temperature of medium, and the study of ultrasound is done when it is propagating through High temperature media of steel. For this purpose, a steel sample is heated from one surface and temperature gradient is generated in it. The methods like Full Matrix Capture (FMC) and Total Focusing Method (TFM) are used to generate real time ultrasonic image at particular instant of time. It is a method to generate the real time two-dimensional ultrasonic image. The FEA model of steel with temperature gradient is created and the ultrasonic inspection methods, FMC and TFM are applied in order to generate the image by taking elementary A-scan data from simulation and plotting the same to generate the image. The simulation results can be validated using high temperature experiments with steel samples and the experimental data can be taken to use TFM algorithm for ultrasonic evaluation. These TFM images provide a visual representation of the steel cross section at elevated temperature at specific instances, facilitating early-stage in-situ monitoring and optimization of the steel solidification process.

Ultrasonic dynamic plane wave imaging for high-speed railway inspection

The detection of internal defects within railway tracks stands as a critical aspect of ensuring transport security, given its potential to precipitate severe accidents. Phased array ultrasonic testing methods have gained extensive usage in rail inspection owing to their remarkable precision and expansive coverage. Traditional ultrasonic inspection techniques typically require either a fixed relative position between the transducer and the specimens or a gentle movement of the transducer to beamform. However, the unavoidable vibration stemming from the vehicle–rail coupled interactions often results in time-variant media, significantly impacting the beamforming. This paper presents a refined wavenumber-domain plane wave imaging technique tailored to enable real-time dynamic imaging, specifically accommodating the vertical vibrational movement of the transducer. Real-time calculation of the transmission focal law is achieved by numerical fitting. The wavenumber-domain technique is utilized to realize real-time accurately reconstructed images. The efficacy of the proposed method is assessed through simulations and experiments performed on a convex cylindrical specimen and a rail head sample, respectively. It can be seen that this approach can achieve a frame rate of 83.3 fps with a pipeline architecture in this study, demonstrating the capability to reconstruct images in vibrating environments, such as during the high-speed inspection of railways.

Utilizing improved YOLOv8 based on SPD-BRSA-AFPN for ultrasonic phased array non-destructive testing

Non-destructive testing (NDT) is a technique for inspecting materials and their defects without causing damage to the tested components. Phased array ultrasonic testing (PAUT) has emerged as a hot topic in industrial NDT applications. Currently, the collection of ultrasound data is mostly automated, while the analysis of the data is still predominantly carried out manually. Manual analysis of scan image defects is inefficient and prone to instability, prompting the need for computer-based solutions. Deep learning-based object detection methods have shown promise in addressing such challenges recently. This approach typically demands a substantial amount of high-resolution, well-annotated training data, which is challenging to obtain in NDT. Consequently, it becomes difficult to detect low-resolution images and defects with varying positional sizes. This work proposes improvements based on the state-of-the-art YOLOv8 algorithm to enhance the accuracy and efficiency of defect detection in phased-array ultrasonic testing. The space-to-depth convolution (SPD-Conv) is imported to replace strided convolution, mitigating information loss during convolution operations and improving detection performance on low-resolution images. Additionally, this paper constructs and incorporates the bi-level routing and spatial attention module (BRSA) into the backbone, generating multiscale feature maps with richer details. In the neck section, the original structure is replaced by the asymptotic feature pyramid network (AFPN) to reduce model parameters and computational complexity. After testing on public datasets, in comparison to YOLOv8 (the baseline), this algorithm achieves high-quality detection of flat bottom holes (FBH) and aluminium blocks on the simulated dataset. More importantly, for the challenging-to-detect defect side-drilled holes (SDH), it achieves F1 scores (weighted average of precision and recall) of 82.50% and intersection over union (IOU) of 65.96%, representing an improvement of 17.56% and 0.43%. On the experimental dataset, the F1 score and IOU for FBH reach 75.68% (an increase of 9.01%) and 83.79%, respectively. Simultaneously, the proposed algorithm demonstrates robust performance in the presence of external noise, while maintaining exceptionally high computational efficiency and inference speed. These experimental results validate the high detection performance of the proposed intelligent defect detection algorithm for ultrasonic images, which contributes to the advancement of the smart industry.

Automated weld defect segmentation from phased array ultrasonic data based on U-net architecture

Ultrasonic inspection is an environmentally friendly and easily deployable nondestructive testing (NDT) method widely used for defect detection of critical components in the industry. Phased array ultrasonic testing (PAUT) is one of the most advanced ultrasonic inspection methods, which gives volume inspection with increased resolution and coverage, improving inspection efficiency. Because of the weld structure echoes and the abstract nature of ultrasound images, especially facing meters and feature-changing weld joints with different thicknesses and welding methods in shipbuilding, the analysis of the PAUT weld data still relies on experienced rater random inspections. Rating complex welded products process is lengthy, costly, and prone to introduce human error during the manual rating but challenges automatic detection. To automatically segment defects in PAUT data, this work shows a combination of PAUT data of ship weld and three-dimensional (3D) U-net architecture. Combining PAUT imaging principles with welding and scan processes, a PAUT volumetric image dataset, including different thicknesses and scan angles, is established. We pioneered the application of 3D U-net architecture to segment defects in PAUT volume data. We found that U-net architecture with two encoding stages will perform better in segmenting defects in PAUT data, and region-based loss mainly improves the accuracy. Furthermore, a lightweight U-net architecture containing skip-connection and residual blocks is proposed with precision and efficiency improvement. The validation results show that the proposed U-net architecture offers a feasible solution to the problem of segmenting defects from PAUT data with a Dice accuracy of 90.9 %. Segmentation results help to locate and measure defects. This method makes locating and sizing defects in PAUT weld data possible within a fraction of a second.

Analysis and denoising of electric noise in phased array ultrasonic testing system

Phased array ultrasonic testing (PAUT) is an advanced technique used for non-destructive testing (NDT) to detect and assess the integrity of materials or structures. In composite materials with significant acoustic attenuation, electrical noise significantly impacts PAUT detection accuracy, highlighting a crucial issue. This paper presents a comprehensive analysis of electrical noise, explains the relationship between electrical noise and inspection parameters, and proposes recommendations to reduce it. A time-varying filter empirical mode decomposition (TVF-EMD) joint wavelet thresholding method is proposed for denoising electrical noise after time-corrected gain (TCG). The ultrasonic echo signal goes through TVF-EMD decomposition, resulting in intrinsic mode functions (IMFs). Energy entropy is utilized to identify the signal-dominant IMFs, and wavelet thresholding is applied to further reduce noise within these selected IMFs. The proposed method effectively removes noise and ensures signal integrity by validating simulated and experimental signals.

Phased Array Ultrasonic Testing on Thick Glass Fiber Reinforced Thermoplastic Composite Pipe Implementing the Classical Time-Corrected Gain Method

Thermoplastic composite pipe is gaining popularity in the oil and gas and renewable energy industries as an alternative to traditional metal pipe mainly due to its capability of being spooled onto a reel and exceptional corrosion resistance properties. Despite its corrosion-proof nature, this material remains susceptible to various defects, such as delamination, fiber breakage, matrix degradation and deformation. This study employed the phased array ultrasonic testing technique with the implementation of the classical time-corrected gain method to compensate for the significant spatial signal attenuation beyond the first interface layer in the thick multi-layered thermoplastic composite pipe. Initially, the ultrasonic signals from the interface layers and back wall were detected with good signal-to-noise ratios. Subsequently, flat-bottom holes of varying depths, simulating one-sided delamination, were bored and the proposed method effectively identified ultrasonic signals from these holes, clearly distinguishing them from the background noise and interface layer signals. Finally, a defect deliberately fabricated within the composite laminate layers during the pipe manufacturing process was successfully identified. Subsequently, this fabricated defect was visualized in a three-dimensional representation using the X-ray computed tomography for a qualitative and quantitative comparison with the proposed ultrasonic method, showing a high level of agreement.