Search Results (102)

Metallic blocks and components are indispensable across the aerospace, energy, and heavy-engineering industries, where undetected internal flaws such as cracks, voids, and inclusions may precipitate catastrophic structural failure. Reliable nondestructive evaluation (NDE) is essential to ensure their integrity and operational safety. Among the available NDE techniques, phased array ultrasonic testing (PAUT) has emerged as one of the most accessible and widely adopted, by virtue of its rapid scanning, electronic beam steering, and capacity to image subsurface defects without disassembly. However, the interpretation of PAUT B-scan images remains hindered by background reflections, material-dependent echo characteristics, and substantial variability in defect size. In this work, a fine-tuned encoder–decoder deep learning network is proposed for the automated segmentation of internal defects in PAUT B-scan images of metallic block specimens. The network couples a ResNet50 encoder with a shallow detail stem, a multi-scale feature fusion module, and a detail refinement block, designed to preserve small defect echoes and sharpen weak defect boundaries characteristic of internal flaws. The proposed approach was compared with five state-of-the-art segmentation architectures, namely FCN, PSPNet, DeepLabv3+, HRNet-OCR, and SegFormer, as well as a conventional Otsu-thresholding baseline representing standard PAUT screening practice. Experimental results demonstrate that the proposed network attained the highest Dice score of 0.7964, defect intersection-over-union of 0.6617, and precision of 0.7094 among all evaluated models, while the Otsu baseline yielded the lowest scores, confirming the benefit of learned segmentation over fixed amplitude thresholding. These findings indicate that the proposed network achieves a favorable trade-off between defect localization accuracy and false-positive suppression, underscoring its potential for reliably segmenting internal defects in PAUT B-scan imaging. Full article

This study investigates the capabilities of modern ultrasonic non-destructive testing (NDT) methods for the detection and sizing of fatigue cracks in hinge bolts used in railway transport. Experimental investigations were conducted using immersion ultrasonic testing combined with advanced signal processing techniques, including PAUT, FMC/TFM, and FMC/PCI. Artificially introduced erosion notches ranging in size from 0.025 mm to 23 mm were used to simulate fatigue defects. The probability of defect detection was evaluated through Hit/Miss analysis and determination of the a90/95 parameter. The results indicate that the FMC/TFM method provides the highest sensitivity for the detection of small defects, while the PAUT technique demonstrates the highest accuracy in defect sizing. Full article

Residual stress (RS) is a key integrity parameter after welding and additive manufacturing, motivating portable sensing methods for in-situ assessment. Phased Array Ultrasonics for Residual Stress (PAURS) treats a phased-array probe as a time-of-flight (ToF) sensor and infers RS from ToF changes of the longitudinal critically refracted (LCR) wave propagating near the surface. In practical deployments, however, the ToF sensing chain can be susceptible to systematic bias from sensor–specimen interface variability (couplant layer thickness) which can dominate the inferred stress uncertainty if not quantified and corrected. This study combines numerical modelling with experimental validation to (i) characterise couplant-induced sensitivity in LCR ToF sensing, (ii) propagate this effect into RS error/uncertainty, and (iii) demonstrate a model-informed compensation strategy suitable for practical calibration workflows. Simulations show that couplant thickness variations can introduce RS errors of ~36 MPa (~13% of yield strength). The proposed compensation reduces ToF bias to 0 ns under idealised simulated conditions and to ~0.3 ns in experiments, corresponding to ~1.1 MPa RS error (~0.4% of yield strength). These results provide configuration-specific guidance for sensor calibration and uncertainty reporting in phased-array ultrasonic RS sensing, and establish a foundation for future in-process sensing of residual stress and microstructure evolution. Full article

Girth welds are susceptible to defects under high internal pressure and stress. While phased array ultrasonic testing (PAUT) is widely used for non-destructive evaluation, manual inspection remains inefficient and highly dependent on expertise. Furthermore, existing deep learning models often struggle with low accuracy and high complexity. This paper proposes a PAUT defect classification method based on YOLOv8. First, median filtering is employed for denoising, and the results show that noise is effectively reduced while preserving key features, achieving PSNR values of 35.132, 35.938, and 36.138 for slag inclusion, pores, and lack of fusion (LOF), respectively. Subsequently, the spatial enrichment algorithm (SEA) is applied to enhance image details without amplifying noise, yielding a PSNR of 33.71 and an SSIM of 0.96. Finally, the YOLOv8 model is implemented for defect recognition. Experimental results demonstrate that the proposed approach achieves a superior balance between precision and recall with high reliability. This method offers a robust and efficient solution for automated PAUT evaluation in practical engineering applications. Full article

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3°$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency. Full article

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article

Hydrogen-Induced Cracking (HIC) and Stress-Oriented Hydrogen-Induced Cracking (SOHIC) pose a significant threat to the integrity of steel components in wet H₂S service within the refining industry. This paper presents an integrated approach to HIC and SOHIC management, combining state-of-the-art inspection and monitoring techniques leading to Fitness-for-Service (FFS) assessments. Specifically, it details the synergistic application of Phased Array Ultrasonic Testing (PAUT) for precise crack detection and characterization and Acoustic Emission (AE) monitoring for real-time damage evolution insights. A key innovation is the development of in-house software capable of automatically clustering HIC and SOHIC PAUT data. An illustrative case study, referring to six months of continuous operational data and advanced re-analysis, demonstrates the practical application and benefits of this approach for predictive maintenance. To the authors’ knowledge, this constitutes the first continuous in-service correlation between real-time AE activity and PAUT-sized HIC damage, providing geometric input for API 579/ASME FFS-1 assessments, minimizing downtime, and mitigating failure risks. Full article

W/EUROFER functionally graded material (FGM) plasma-sprayed coatings are used as a protective layer in nuclear fusion applications. It is vital to develop a non-destructive test method to analyze interface characteristics and detect delamination in coatings. A phased array ultrasonic test method was developed in this work to analyze the coating interface characteristics. Two types of coated samples were tested: first, a W/EUROFER FGM-coated flat small sample, and secondly, a large-scale L-shape 50% W and 50% EUROFER curve-coated sample. The phased array ultrasonic test method reliably detected two separate interfaces in W/EUROFER FGM coating, and no delamination was detected, which was verified by cross-sectional image analysis. Secondly, the phased array ultrasonic test precisely detected delamination created during deposition in a large-scale L-shape 50% W and 50% EUROFER curve coated sample. The accuracy in detecting delamination was verified by cross-sectional images of the interface. The phased array ultrasonic test was found to be a reliable method for detecting delamination in multilayer coatings from small-scale to large-scale curved components. Full article

Welding-based manufacturing and joining processes are extensively used in various areas of industrial production. While welding has been used as a primary method of joining in many applications, its capability to fabricate metal components such as the Wire Arc Additive Manufacturing (WAAM) method should not be undermined. WAAM is a promising method for producing large metal parts, but it is still prone to defects such as porosity that can reduce structural reliability. To ensure these defects are found and measured in a consistent way, inspection methods must be tied directly to code-based acceptance limits. In this work, a three-pass WAAM joint specimen was made in a welded-joint configuration using robotic GMAW-based deposition. This setup provided a stable surface for Phased Array Ultrasonic Testing (PAUT) while still preserving WAAM process conditions. The specimen, which was intentionally seeded with porosity, was divided into five zones and inspected using the 6 dB drop method for defect length and amplitude-based classification, with AWS D1.5 serving as the reference code. The results showed that porosity was not uniform across the bead. Zones 1 and 3 contained the longest clusters (15 mm and 16.5 mm in length) and exceeded AWS length thresholds, while amplitude-based classification suggested they were less critical than other regions. This difference shows the risk of relying on only one criterion. By embedding these results in a DMAIC (Define–Measure–Analyze–Improve–Control) workflow, the inspection outcomes were linked to likely causes such as unstable shielding and cooling effects. Overall, the study demonstrates a code-referenced, dual-criteria approach that can strengthen quality control for WAAM. Full article

Additive manufacturing (AM) enables the fabrication of complex components with high geometric freedom, but it can introduce near-surface flaws due to rapid solidification, resulting in porosity and lack of fusion. In addition, localized melting and steep thermal gradients favor the formation of micro-cracks. Conventional ultrasonic techniques have shortcomings in detecting such flaws because of front-wall interference, further affected by surface roughness and anisotropy. This study evaluates the effectiveness of the Total Focusing Method (TFM), an advanced ultrasonic imaging technique implemented in Full Matrix Capture (FMC), for near-surface flaw detection in Laser Powder Bed Fusion (LPBF) AM components. To assess TFM performance, subsurface side-drilled holes (SDHs) in AM Ti-5Al-5V-5Mo-3Cr (Ti-5553) material were used as the reference reflectors and compared with Phased Array Ultrasonic Testing (PAUT) under identical conditions. Results showed that TFM achieved higher spatial resolution and more reliable detection of shallow flaws, successfully detecting features as shallow as 0.40 ± 0.05 mm below the surface, whereas PAUT was limited to greater depths. These findings demonstrate TFM as a reliable non-destructive evaluation method for shallow flaws in AM parts, while contributing one of the first systematic comparative datasets of PAUT and TFM for shallow SDHs in LPBF titanium alloys. Full article

The full multilayer high-pressure hydrogen storage vessel plays an important role in hydrogen refueling stations. However, these vessels may fail after a certain period due to crack formation, necessitating periodic inspections. Among the various parts, the butt joints connecting the thick-walled nozzles and hemispherical heads represent critical and challenging areas for inspection. In this study, a one-shot multi-receiver defect detection and localization method is developed based on the ultrasonic phased array method. In order to verify the feasibility of the method, the interaction between the ultrasonic wave and the crack defects at the key position of the butt joint is analyzed based on finite element, enabling the accurate localization of crack tips; an experimental specimen was designed and fabricated, and a corresponding phased array detection test was conducted to validate the method. Full article

This paper presents a comprehensive study on automated defect detection in complex structures using phased array ultrasonic testing data, focusing on both traditional signal processing and advanced deep learning methods. As a non-AI baseline, the well-known signal-to-noise ratio algorithm was improved by introducing automatic depth gate calculation using derivative analysis and eliminated the need for manual parameter tuning. Even though this method demonstrates robust flaw indication, it faces difficulties for automatic defect detection in highly noisy data or in cases with large pore zones. Considering this, multiple DL architectures—including fully connected networks, convolutional neural networks, and a novel Convolutional Attention Temporal Transformer for Sequences—are developed and trained on diverse datasets comprising simulated CIVA data and real-world data files from welded and composite specimens. Experimental results show that while the FCN architecture is limited in its ability to model dependencies, the CNN achieves a strong performance with a test accuracy of 94.9%, effectively capturing local features from PAUT signals. The CATT-S model, which integrates a convolutional feature extractor with a self-attention mechanism, consistently outperforms the other baselines by effectively modeling both fine-grained signal morphology and long-range inter-beam dependencies. Achieving a remarkable accuracy of 99.4% and a strong F1-score of 0.905 on experimental data, this integrated approach demonstrates significant practical potential for improving the reliability and efficiency of NDT in complex, heterogeneous materials. Full article

Classical active thermographic testing of industrial goods has mostly been limited to generating 2D defect maps. While for surface or near-surface defect detection, this is a desired result, for deeply buried defects, a 3D reconstruction of the defect geometry is coveted. This general trend can also be well observed in widely used NDT methods (radiography, ultrasonic testing), where the progression from 2D to 3D reconstruction methods has already made profound progress (CT, UT phased array transducers). Achieving a fully 3D defect reconstruction in active thermographic testing suffers from the diffusive nature of thermal processes. One possible solution to deal with thermal diffusion is the application of the virtual-wave concept, which, by solving an inverse problem, allows the diffusiveness to be extracted from the thermographic data in the post-processing stage. What is left follows propagating-wave physics, enabling the usage of well-known algorithms from ultrasonic testing. In this work, we present our progress in the 3D reconstruction of deeply buried defects using spatially structured laser heating in conjunction with applying the well-known total focusing method (TFM) in the virtual-wave domain. Full article

The degree of anisotropy and heterogeneity in coarse-grained materials significantly affects ultrasonic propagation behavior and scattering. This paper proposes a vector coherent factor threshold total focusing imaging method (VCF-T-TFM) for austenitic stainless steel, based on material properties, through a combination of simulation and experimentation. Three types of austenitic stainless steel weld test blocks with varying degrees of heterogeneity were selected containing multiple side-drilled hole defects, each with a diameter of 2 mm. Full-matrix data were collected using a 32-element phased array probe with a center frequency of 5 MHz. The grain size and orientation of the material were quantitatively observed via electron backscatter diffraction (EBSD). By combining the instantaneous phase distribution of the TFM image, the coarse-grained material coherence compensation value (C_A) and probability threshold (P_T) were optimized for different heterogeneous regions, and the vector coherence imaging threshold (γ) was adjusted. The defect imaging results of homogeneous material (carbon steel) and three austenitic stainless steels with different levels of heterogeneity were compared, and the influence of coarse-grained, anisotropic heterogeneous structures on the imaging signal-to-noise ratio was analyzed. The results show that the VCF-T-TFM, which considers the influence of material properties on phase coherence, can suppress structural noise. Compared to compensation results that did not account for material properties, the signal-to-noise ratio was improved by 97.3%. Full article

This study explores the application of low-power vibrothermography (LVT) for detecting barely visible impact damage (BVID) in carbon fibre-reinforced polymer (CFRP) laminates. Composite specimens with varying impact energies (2.5–20 J) were excited using a single piezoelectric transducer with a nominal centre frequency of 28 kHz, operated at a fixed excitation frequency of 28 kHz. Thermal data were captured using an infrared camera. To enhance defect visibility and suppress background noise, the raw thermal sequences were processed using principal component analysis (PCA) and robust principal component analysis (RPCA). In LVT, RPCA and PCA provided comparable signal-to-noise ratios (SNR), with no consistent advantage for either method across all cases. In contrast, for pulsed thermography (PT) data, RPCA consistently resulted in higher SNR values, except for one sample. The LVT results were further validated by comparison with PT and phased array ultrasonic testing (PAUT) data to confirm the location and shape of detected damage. These findings demonstrate that LVT, when combined with PCA or RPCA, offers a reliable method for identifying BVID and can support safer, more efficient structural health monitoring of composite materials. Full article