Search Results (184)

Ultrasonic pulse-echo imaging is one of the most widely used non-destructive evaluation (NDE) modalities for monitoring the structural integrity of reinforced-concrete civil infrastructure such as bridge decks, tunnel linings, and dam walls. In this acquisition geometry, a single low-frequency transducer is mechanically raster-scanned over the accessible top surface of the specimen and records one A-scan per scan position, simultaneously serving as transmitter and receiver. However, commonly used reconstruction algorithms such as the Synthetic Aperture Focusing Technique (SAFT) and Reverse Time Migration (RTM) tend to produce reconstructions of limited quality on this class of data because they do not adequately model the round-trip propagation kernel that is specific to the monostatic geometry, they do not separate the strong near-surface direct-arrival reflection from the bulk image, and they do not account for the persistent aggregate-induced clutter that contaminates every A-scan in concrete media. In this paper, we propose a clutter-aware reconstruction method for monostatic ultrasound acquisition (CARMA), whose main innovation is the joint integration of a monostatic-specific round-trip propagation model, a dedicated near-surface direct-arrival subspace, and a data-adaptive low-rank clutter subspace within a unified model-based reconstruction framework. Unlike existing reconstruction approaches, CARMA explicitly accounts for the co-located transmit–receive geometry through a squared-cosine round-trip directivity model while simultaneously separating scan-dependent direct-arrival contributions and aggregate-induced clutter from the desired reflectivity image. To verify the method under fully controlled and repeatable conditions, we generate intensive, physically realistic full-wave simulations with the k-Wave pseudo-spectral acoustic solver that reproduce a representative civil-infrastructure inspection scenario: three reinforced-concrete specimens with a stepped back wall of varying thickness, ten embedded ground-truth defects spanning steel tendon ducts and low-impedance polystyrene inclusions, a monostatic raster-scanned pulse-echo acquisition, and randomly distributed aggregate scatterers that reproduce the clutter of real concrete. Results on these intensive k-Wave simulations indicate that CARMA reconstruction yields approximately $2\times$ lower localization error than RTM and approximately $4\times$ lower localization error than SAFT, while recovering the deepest embedded defect with substantially better localization and contrast than the comparison methods. Full article

Non-destructive evaluation (NDE) methods are widely used to assess concrete quality, but their interpretation in reinforced concrete members remains challenging because measured responses may be influenced by reinforcement layout, casting and surface conditions, and testing configuration. This study investigates the effects of longitudinal reinforcement type, stirrup spacing, and stirrup material on Schmidt rebound hammer (SRH) and ultrasonic pulse velocity (UPV) responses of concrete beams. Nine beams were cast from the same concrete batch: one plain concrete reference beam, steel-reinforced beams, GFRP-reinforced beams, and hybrid steel–GFRP-reinforced beams. Two stirrup spacings, 100 and 200 mm, and two stirrup materials, steel and GFRP, were considered. In total, 1044 rebound readings and 162 UPV measurements were collected, with UPV testing performed using Right–Left direct, longitudinal direct, and Right–Left indirect configurations. Compared with the plain reference beam, the mean rebound number (RN) increased by up to 21.5% in steel-reinforced beams and 23.2% in hybrid beams, whereas the GFRP-reinforced beam with wider stirrup spacing showed a slight reduction of about 0.8%. UPV differences were more moderate, with a maximum increase of approximately 10% relative to the plain beam. Rebound results also showed a clear face effect, with the bottom face producing the highest values and the top face the lowest. The dense spatial mapping captured local variability within each beam; however, because one beam represented each configuration, the comparisons are interpreted as descriptive specimen-level trends rather than generalized reinforcement effects. Single-pair spacing comparisons indicated larger and more variable differences in RN than in UPV between the 100 and 200 mm specimens. These findings demonstrate the importance of documenting casting face, reinforcement proximity, measurement location, and transmission path when interpreting NDE results in hybrid-reinforced concrete members. Full article

The fatigue degradation of polymer-matrix composite materials, especially in the case of thermomechanical fatigue, when the self-heating effect dominates the process of the cyclically bent structure in fully reversed mode, results in complex damage patterns with a heterogeneous nature. It is therefore essential to evaluate the structural condition of tested objects in a non-destructive way to predict their fatigue lifetime. One of the most accurate non-destructive testing techniques used for such examinations is X-ray computed tomography, which enables the evaluation of the resulting fatigue damage in a three-dimensional space. However, the main challenge in the evaluation of such results is the attribution of the observed damage to specific types, the assessment of its severity, and its quantitative analysis. In this study, we have proposed a novel framework based on intensity and morphological features for the classification of damage types and a 3D U-Net-based approach for segmentation. The acquired results demonstrated the high accuracy of the performance evaluation indices, which exceeded 99% for the proposed framework, allowing effective evaluation of the resulting damage and being potentially helpful in the assessment of structural performance and residual fatigue life. Such analysis contributes to the trends of NDE 4.0 and adaptive mechanics, where composite structures are tailored to specific operational conditions to reach the target lifecycle. Full article

Interfacial coating loss in bitumen-coated aggregates is difficult to quantify because the exposed aggregate regions are spatially heterogeneous, visually subtle, and sensitive to illumination and background conditions. This study develops a fluorescence-based optical nondestructive evaluation (NDE) framework for quantitative assessment of bitumen–aggregate stripping under standardized laboratory imaging conditions. A dataset of 5760 fluorescence images was collected from 120 physical BAP specimens representing 20 bitumen–aggregate combinations, with six viewing directions recorded for each specimen under eight acquisition environments. A lightweight no-reference image quality assessment model (LAR-IQA), together with visual inspection of shadow suppression and segmentation robustness, was used to select a reference acquisition protocol. The black-background, UV plus natural-light, glass-enclosure configuration provided stable contrast while reducing shadow interference. Three candidate segmentation methods were benchmarked against manually annotated reference masks, and the HSVSC algorithm achieved the best overall performance, with the highest mean Dice coefficient of 0.73. Using the standardized sensing–processing workflow, the image-derived stripping ratio distinguished material-dependent coating-loss behavior and revealed significant sensitivity to acquisition parameters. Aggregate type dominated the measured response under the tested conditions, with the mean stripping level ranked as limestone (3.94%) < quartz fine sandstone (4.17%) < basalt (4.95%) < granite (15.56%). ANOVA based on the 20 combination-level mean stripping ratios derived from 120 specimens, together with grey relational analysis, further indicated that, within the tested material set, aggregate-related descriptors were more strongly associated with the measured stripping variation than the selected bitumen descriptors. The proposed framework provides a repeatable, non-contact optical NDE route for converting subjective visual stripping assessment into image-derived quantitative measurement. Full article

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 review traces the four-generation evolution of non-destructive evaluation (NDE 1.0–4.0) and audits where the field genuinely stands today. The central finding is that statistically qualified probability of detection (POD), as defined in MIL-HDBK-1823A and related frameworks, is not interchangeable with machine-learning metrics such as accuracy or F1-score; the two answer different questions and rest on different statistical foundations. Reported AI performance on curated datasets does not, by itself, predict field reliability because domain shift, sensor variability, and class imbalance change the inspection signal once a model leaves the lab. Six recurring barriers limit industrial uptake: scarce open benchmark datasets, domain shift, weak interoperability, explainability constraints, cybersecurity exposure, and the lack of broadly accepted code provisions for AI-derived accept/reject decisions. The oil and gas sector is used as a case study because it combines high inspection volume, severe operating environments, mature risk-based inspection practice, and strong regulatory conservatism. NDE 4.0 is technically credible; its wider acceptance in safety-critical industries will be earned through representative field validation, auditable model governance, standardised data structures, and qualification pathways—not through stronger laboratory accuracy claims. Full article

Magnetostrictive patch transducers (MPTs) are highly efficient for generating and detecting ultrasonic waves for non-destructive evaluation (NDE), though their use on cementitious media and fibre-reinforced concrete has not yet been investigated. In this study, a COMSOL simulation, validated with laser-Doppler vibrometry, was first used to quantify patch deformation for use in subsequent simulation of wave propagation in samples. The MPT system was then validated on thin glass plates, producing tunable A₀, S₀, and SH₀ modes through frequency-wavelength matching. In cementitious mortar plates, SH₀ and SH₁ modes were demonstrated experimentally for the first time using MPTs. The validated COMSOL model was then used to interpret complex signals in quasi-plate and half-space cementitious mortar prisms, showing that MPTs generate Rayleigh, bulk SH, and surface-skimming SH modes. In steel fibre-reinforced concrete, surface-skimming SH wave speed correlated with increases in breaking strength even in the presence of surface features such as notches. Notably, Rayleigh wave speeds could not be measured in the presence of surface features, and the Rayleigh velocities measured in the same sample, but not in the local tested area did not correlate with SH speed. This behaviour is likely due to the non-uniform distribution of material constituents, including fibre-reinforcement and coarse aggregate, combined with the different propagation paths and depth sensitivities of the reported wave modes. Overall, racetrack-coil MPTs enable multimodal inspection of cementitious media, providing information on the presence of geometric features and material properties. Full article

This study presents an approach to quantifying impact-induced damage severity in composites, focusing on synthetic carbon fibre-reinforced polymer (CFRP), natural flax fibre-reinforced polymer (FFRP) and hybrid fibre reinforced polymer (HFRP) composite of carbon and flax. The investigation aims to quantitatively characterise impact damage under energies ranging from 10 to 70 J through acousto–ultrasonics (AU) testing, proposing an efficient technique for evaluating the integrity of various FRP composites under in-service conditions. AU testing was performed at azimuthal angles of 0°, 30°, 45°, 60° and 90°, utilising acousto–ultrasonic waveform indices (AUWIs), such as wave velocity, peak amplitude, energy content, centroid frequency and skewness factor. The damage severity index is correlated with the damage mode. The findings establish that wave velocity is a reliable parameter for quantifying damage severity across all composite material types considered, with high adjusted R² values of 0.92 for CFRP, 0.89 for FFRP and 0.90 for HFRP. Peak amplitude also shows considerable sensitivity. Finally, this research highlights the limitations of traditional non-destructive evaluation (NDE) techniques and demonstrates the potential of combining multi-damage metrics with advanced imaging methods, such as X-ray micro-computed tomography (X-ray µCT) and scanning electron microscopy (SEM), to provide a comprehensive assessment of damage in various composite materials. The proposed methodology offers a promising approach for quantifying the impact damage severity in composite structures, as applicable to wind turbine blades, amongst other structural components. Full article

Testing harvested logs is a critical step in the wood products supply chain. Non-destructive evaluation (NDE) methods are essential for grading and sorting logs, especially given variations associated with tree age. In this study, plantation-grown Eucalyptus nitens from two age groups were sourced from two Tasmanian harvesting sites for NDE and comparison with destructive stiffness testing. The key finding is that the correlation between dynamic modulus of elasticity (DMOE) and static modulus of elasticity (MOE) weakens with increasing age, particularly at the whole-log level. For further analysis, the radial location of recovered small clear samples (from pith to bark) was examined. Core samples (near the pith) showed the strongest correlation between DMOE and static MOE (R² = 0.51), followed by middle (R² = 0.46) and outer samples (R² = 0.25). This study demonstrates that considering the radial location of recovered samples is a more effective approach for improving grading accuracy. Age is a key factor for initial segregation of logs before applying NDE for property analysis of both logs and recovered samples. Full article

This paper presents a fully automated platform for real-time monitoring of fatigue-induced microstructural changes in metallic materials, using Rayleigh surface waves and Laser Doppler Vibrometry (LDV). The system integrates ultrasonic excitation, non-contact optical sensing, and high-speed signal processing in a unified LabVIEW environment. Rayleigh waves are generated via a contact transducer, while LDV captures surface vibrations with sub-nanometric velocity resolution, ensuring repeatability and eliminating coupling variability. The software automates synchronization, deterministic data acquisition, filtering, FFT analysis, and extraction of nonlinear coefficients (β₂, β₃) at high execution rates without the need for post-processing. Experimental validation under cyclic loading revealed a clear sensitivity hierarchy: the Rayleigh wave velocity remained invariant, the acoustic attenuation responded gradually, while the nonlinear parameters exhibited the earliest and steepest response to fatigue damage, confirming their superiority as early-stage indicators. The system offers low-latency timing, long-term stability, and modular design, establishing a robust data-streaming foundation that can support future integration with digital twin frameworks and machine learning models. Furthermore, the acoustic findings were successfully cross-validated using Infrared Thermography, which confirmed the critical damage transition phase. This work bridges nonlinear acoustics and software automation, providing a scalable diagnostic solution for predictive maintenance within structural health monitoring systems. Full article

Coated ferromagnetic conductors (CFCs) are widely used in the engineering field, such as transportation, petrochemicals, energy, etc. Owing to long-term exposure to harsh and corrosive environments, involving large temperature differences, cyclic loading and humidity, hidden corrosion occurring under the coatings of CFCs has been found to be one of the most critical defects posing a severe threat to the structural strength and safety of CFCs. Therefore, it is important to conduct rapid imaging and quantitative evaluation of this hidden corrosion via Non-Destructive Evaluation (NDE) techniques. A magnetic field-viewing film (MFVF) characterizes magnetic fields by displaying corresponding color shifts, offering a direct visual representation of the magnetic field intensity. In light of this, this paper proposes an MFVF-based probe composed of multiple micro-sensor units for fast imaging of hidden corrosion in CFCs. An image-processing technique based on the modified Canny algorithm is subsequently proposed for identification of corrosion opening profiles in MFVF images. Based on the identification results, an assessment of hidden corrosion parameters is conducted. It is inferred from the experimental results that the opening area, depth and volume of hidden corrosion can be quantitatively evaluated, with an average accuracy of 86.1%. Full article

Tensile and compressive stresses generated during the exploitation of wheel rims can lead to significant failures, posing risks to safety and the environment. Among non-destructive evaluation (NDE) methods, ultrasonic velocity measurement has become widely used for assessing stress states in critical rail vehicle components such as wheel rims. In this study, the relationship between ultrasonic wave velocity and applied compressive stresses in aluminum (EN AW-2011) and austenitic stainless steel (1.4301) specimens is investigated. The methodology integrates ultrasonic time-of-flight (TOF) measurements with controlled mechanical loading up to the elastic limit. The results show that ultrasonic velocity increases with applied compressive stress, with an average change of approximately 40 m/s between unloaded and maximum loading conditions. The material type was identified as the dominant factor, with velocity differences of up to 800 m/s between aluminum and steel, while the applied load contributed changes of approximately 200 m/s. Statistical analysis using Design of Experiments (DOE) and ANOVA confirmed the significance of all main factors (p < 0.0001). The findings demonstrate the sensitivity of ultrasonic velocity to elastic stress states and provide a quantitative basis for the development of reliable in situ ultrasonic stress monitoring systems in rail applications. Full article

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks. Full article

Carbon Fiber Reinforced Polymers (CFRP) are extensively utilized in high-performance engineering, yet localized structural discontinuities can severely compromise their integrity. This paper aims to achieve high-sensitivity characterization of such anomalies using a proposed acoustic shearography technique based on continuous acoustic excitation. A comprehensive finite element model (FEM) was developed to clarify the mechanical-energy coupling between the acoustic fields and localized surface strain field modulations. By exploiting ultrasonic energy coupling, the localized features of discontinuities were identified through full-field, non-contact optical measurement of localized phase distortions. Key parameters, including shearing amount, excitation frequency, driving voltage, and geometric characteristics of blind flat-bottom holes (BFBH), were systematically investigated. The results demonstrate a high correlation between FEM simulations and experimental observations quantitatively elucidating how defect diameter and hole depth modulate surface strain distributions. The proposed hybrid acoustic optical approach achieves near-instantaneous full field imaging within a millisecond timeframe typically under 200 ms. Additionally, the methodology leverages localized acoustic resonance to significantly boost the signal-to-noise ratio (SNR) resulting in highly quantified phase map contrast. Full article

To enhance the measuring capabilities of modern Non-Destructive Evaluation (NDE) devices, it has become essential to integrate standardized digitization services and industry-compliant functionalities. This perspective paper examines approaches for improving NDE systems by incorporating key Industry 4.0 technologies, specifically digital representations such as the Asset Administration Shell (AAS) and OPC UA (Open Platform Communications Unified Architecture). We discuss requirements for interoperable, semantically rich descriptions of NDE systems, outline how OPC UA information models and AAS submodels can be combined with MQTT-based transport, and illustrate these concepts through representative prototype implementations, including predictive maintenance and chatbot assistant use cases. By leveraging these technologies, NDE devices can be transformed into interoperable, data-rich, and intelligent components within smart industrial ecosystems. Compared with previous studies, this Perspective is the first to systematically bring together the requirements, architectural patterns, and evaluation criteria for digital representations designed specifically for NDE systems. It also provides, in a practical and accessible way, NDE-focused OPC UA and AAS-based architectures that support both predictive maintenance and LLM-assisted operator guidance. The presented implementations are at an early stage and serve as illustrative examples, while systematic quantitative validation is ongoing and is outlined as future work. Full article