Search Results (695)

To address the prediction of fatigue damage for 3D-printed Carbon Fiber Reinforced Polymer (CFRP), this study used 3D-printing technology to fabricate CFRP specimens. Through multi-stage fatigue testing, samples with varying porosity levels were obtained. Based on porosity test results and ultrasonic attenuation coefficient measurements of specimens under different fatigue cycle counts, a quantitative relationship model was established between the porosity and ultrasonic attenuation coefficient of 3D-printed CFRP. According to the porosity and fatigue-loading cycles obtained from tests, the Time-series Generative Adversarial Network (TimeGAN) algorithm was employed for data augmentation to meet the requirements for neural-network training. Subsequently, the Long Short-Term Memory (LSTM) neural network was utilized to predict the fatigue damage evolution of 3D-printed CFRP specimens. Research findings indicate that by integrating the established relationship between porosity and ultrasonic attenuation coefficient, non-destructive testing of material fatigue damage evolution based on ultrasonic attenuation coefficient can be achieved. Full article

Pipelines, as critical carriers for energy transportation, are prone to defects such as cracks and corrosion during long-term operation. Traditional testing methods exhibit limitations in various aspects, while electromagnetic ultrasonic testing technology, leveraging its advantages of non-contact operation and couplant-free application, has emerged as a significant direction for pipeline integrity assessment. This paper analyzes the advantages of EMAT guided wave testing technology in achieving long-distance and rapid screening of pipelines, as well as the strengths of bulk wave testing technology in high-precision quantitative evaluation. It also examines the unique value of obliquely incident SV waves in the directional identification of weld defects. Furthermore, the paper discusses the potential of integrating EMAT with multiple technologies, demonstrating how multi-physical field synergy enhances detection reliability. Finally, it summarizes the remaining challenges in practical engineering applications, providing references for advancing the field toward intelligent and high-precision development. Full article

Z-cut lithium niobate single crystal demonstrates considerable promise for contact-based ultrasonic nondestructive testing and structural health monitoring (SHM) transducers due to its high piezoelectric coefficients, strong electromechanical coupling capability, and environmentally friendly lead-free composition. As a simulation-based theoretical exploration, this study systematically investigates the impact of gap spacing and electrode width in concentric ring configurations on the resonant characteristics and pulse-echo response of ultrasonic transducers by establishing a parametrized finite element model. Numerical simulations reveal that electrode geometry plays a critical role in determining both the effective electromechanical coupling coefficient and echo signal strength. Optimizing the electrode ring width achieved an effective electromechanical coupling coefficient ($k_{\mathit{eff}}$) of 35.2%, while systematic enlargement of the electrode gap further enhanced this value to 50.8%. The study also demonstrates that optimized ring width and adjusted electrode spacing increased the echo signal’s peak-to-peak amplitude ($V_{pp}$) by factors of 4.94 and 2.03, respectively, compared to the poorest-performing configuration within each parameter group. This study establishes that precise design of concentric electrode configurations serves as an effective strategy for tuning lithium niobate ultrasonic transducer characteristics, providing critical design guidelines for developing high-performance ultrasonic transducers for solid medium coupling. Full article

The porosity of additive manufacturing components significantly impacts their mechanical properties, thereby limiting their widespread application in engineering. Current porosity assessment predominantly relies on destructive testing, underscoring the urgent need for accurate in situ non-destructive testing methods. In this paper, we propose a novel deep learning-assisted non-destructive testing method for porosity assessment in additive manufacturing components. Our approach leverages the high sensitivity of ultrasonic coda waves to minute internal material changes, combined with the powerful feature extraction capability of deep learning. Experimental results demonstrate that ultrasonic coda waves are sensitive to porosity variations in additive manufacturing components. Due to the porosity of additive manufacturing components involves multi-dimensional micro-structural features, conventional parameters such as the correlation coefficient and relative velocity change cannot establish an effective mapping relationship, despite their variation with porosity, thus precluding accurate inversion. To address this challenge, we propose a coda–convolutional neural network–multi-head attention mechanism network. Ultrasonic coda waves can fully interact with pores inside additive manufacturing components, and their signals are rich in porosity-related features. The introduction of deep learning significantly enhances the ability to extract such features. The trained network achieves high-precision porosity prediction with an accuracy of 98%. Our proposed approach reveals the complementary integration of ultrasonic coda waves and deep learning methods: the former provides high sensitivity to porosity changes, while the latter addresses the limitations of difficult extraction of relevant features and unclear complex mapping relationships. This collaborative framework establishes a new solution for high-precision non-destructive testing of additive manufacturing components. Full article

Non-destructive evaluation techniques are increasingly recognised as effective alternatives to destructive testing for estimating the compressive strength of lightweight aggregate concrete (LWAC). Among these, ultrasonic pulse velocity (UPV) is a well-established and widely employed method, characterised by its speed, non-invasiveness, and relative simplicity of implementation. In this study, an experimental dataset comprising 640 core segments from 160 cylindrical specimens, provided for analysis, was investigated. Each segment was described by physical and processing variables or features, including lightweight aggregate (LWA) and concrete densities, casting and vibration times, experimental dry density, and P-wave velocity obtained through UPV testing. A segregation index, derived from UPV measurements and defined as the ratio of local to mean P-wave velocity within each specimen, was also considered, following approaches previously suggested in the literature. A range of machine learning techniques was applied to assess the predictive capacity of local P-wave velocity and segregation index. Most ensemble-based methods and support vector regression (SVR) achieved the highest predictive performance when the segregation index was excluded, suggesting that its inclusion did not improve the predictive ability of the models. By contrast, Gaussian process regression (GPR) showed slight improvements when the segregation index was included. The results confirmed that the P-wave velocity measured by UPV testing is a reliable non-destructive predictor of compressive strength in LWAC. At the same time, the added value of the segregation index remained negligible under conditions of low segregation, as reflected by segregation index values above 0.8. These findings highlight the practical potential of integrating UPV-based measurements with data-driven modelling to enhance the reliability of concrete characterisation and quality control. Full article

This study presents the first application of an automatic ultrasonic mapping system for the assessment of natural consolidants applied to replicas of painted wall surfaces. In Cultural Heritage conservation, evaluating consolidation efficiency remains a critical issue, particularly for substrates characterized by high porosity, heterogeneity, and mechanical fragility. Ultrasonic testing offers a fully non-contact diagnostic approach capable of detecting variations in cohesion, stiffness, and internal discontinuities, thus overcoming the limitations of semi-invasive mechanical procedures. Three polysaccharide-based consolidants—Arabic gum, Funori, and Opuntia ficus-indica mucilage—were applied to wall-painting replicas prepared according to historically documented techniques. Their performance was investigated through a comparative methodology combining a peeling test with non-contact air-coupled ultrasonic probes. Results indicate that Opuntia mucilage, although still at an experimental stage, provides significant improvements in cohesion, confirming its potential as a sustainable and substrate-compatible alternative to conventional consolidants. By demonstrating the complementary nature of ultrasonic mapping and peeling tests, this work contributes to the development of reproducible, non-invasive diagnostic strategies for evaluating consolidation treatments, particularly on fragile surfaces where conventional mechanical testing is unsuitable. Full article

This study proposes a field-calibrated, NDT-integrated BIM modeling framework to improve the reliability of post-earthquake assessment for reinforced concrete (RC) buildings. The approach combines destructive and nondestructive testing (NDT) data—including core drilling, Schmidt hammer, ultrasonic pulse velocity (UPV), and Windsor probe—through a site-specific WinSonReb regression model. The calibrated material properties (average compressive strength ≈ 18.6 MPa, CoV > 20%) were embedded into a Building Information Modeling (BIM) environment, producing an as-is, NDT-calibrated BIM model representing a Level-2 static digital twin of the structure. Nonlinear static pushover analyses performed in accordance with TBDY-2018 and ASCE 41-17 showed that the calibrated model exhibits a fundamental period of 0.85 s—approximately 18% longer than the uncalibrated BIM model. This elongation increased displacement demand and caused a shift in performance classification: while the uncalibrated model indicated Life Safety (LS), the calibrated model predicted behavior approaching Collapse Prevention (CP) in the Y direction. Furthermore, calibration reversed the predicted damage hierarchy, from ductile beam hinging to brittle column- and wall-controlled failure near elevator openings, consistent with post-event observations from the 2023 Kahramanmaraş earthquakes. These results demonstrate that integrating field-calibrated NDT data into BIM-based seismic models fundamentally alters both strength estimation and failure-mechanism prediction, reducing epistemic uncertainty and providing a more conservative basis for retrofit prioritization. Although demonstrated on a single case study, the proposed workflow offers a realistic and scalable pathway for NDT-supported seismic performance assessment of existing RC buildings. Full article

This paper describes how the thermal treatment of 3D-printed PLA samples, fabricated by Fused Deposition Modeling (FDM), affects elastic properties by means of vibrational spectroscopy and ultrasonic echography. Longitudinal and shear sound velocities were measured experimentally to determine Young’s, bulk, shear, and longitudinal moduli, as well as Poisson’s ratio. The results were complemented with two different simulation approaches—the elastodynamic finite integration technique (EFIT) and the equivalent electric analog technique implemented with LPSpice—whose predictive performance was assessed using statistical performance metrics. The circuit-based simulation method demonstrated superior agreement with experimental behavior compared to EFIT. Both measured and simulated data reveal that PLA chains undergo overall structural strengthening and enhanced packing up to 2 h of heating, followed by a clear reduction in these enhancements as thermal degradation emerges with further heating. Poisson’s ratio remained relatively stable throughout, indicating minimal impact on strain distribution characteristics despite observable stiffening and subsequent softening. Vibrational ATR (Attenuated Total Reflection) spectra corroborated these findings through systemic shifts in C-COO, C-O-C, and C-O stretching modes associated with the same structural modifications. Overall, this combined experimental–simulation framework provides an integrated understanding of thermally induced mechanical and molecular evolution in 3D-printed PLA relevant to defense applications. Full article

Distributed fiber-optic photoacoustic non-destructive testing (DFP-NDT) represents a paradigm shift from passive sensing to active probing, fundamentally transforming structural health monitoring through integrated fiber-based ultrasonic generation and detection capabilities. This review systematically examines DFP-NDT’s evolution by following the technology’s natural progression from fundamental principles to practical implementations. Unlike conventional approaches that require external excitation mechanisms, DFP-NDT leverages photoacoustic transducers as integrated active components where fiber-optical devices themselves generate and detect ultrasonic waves. Central to this technology are photoacoustic materials engineered to maximize conversion efficiency—from carbon nanotube-polymer composites achieving 2.74 × 10⁻² conversion efficiency to innovative MXene-based systems that combine high photothermal conversion with structural protection functionality. These materials operate within sophisticated microstructural frameworks—including tilted fiber Bragg gratings, collapsed photonic crystal fibers, and functionalized polymer coatings—that enable precise control over optical-to-thermal-to-acoustic energy conversion. Six primary distributed fiber-optic photoacoustic transducer array (DFOPTA) methodologies have been developed to transform single-point transducers into multiplexed systems, with low-frequency variants significantly extending penetration capability while maintaining high spatial resolution. Recent advances in imaging algorithms have particular emphasis on techniques specifically adapted for distributed photoacoustic data, including innovative computational frameworks that overcome traditional algorithmic limitations through sophisticated statistical modeling. Documented applications demonstrate DFP-NDT’s exceptional versatility across structural monitoring scenarios, achieving impressive performance metrics including 90 × 54 cm² coverage areas, sub-millimeter resolution, and robust operation under complex multimodal interference conditions. Despite these advances, key challenges remain in scaling multiplexing density, expanding operational robustness for extreme environments, and developing algorithms specifically optimized for simultaneous multi-source excitation. This review establishes a clear roadmap for future development where enhanced multiplexed architectures, domain-specific material innovations, and purpose-built computational frameworks will transition DFP-NDT from promising laboratory demonstrations to deployable industrial solutions for comprehensive structural integrity assessment. Full article

Quick and reliable in situ non-destructive assessment of the material structure is especially critical in the case of measurement of rail defects concerning the demands of quick, uninterrupted transportation and safety. This paper presents the test results of a patented measuring head that is able to perform ultrasonic rail defect detection at speeds of up to 120 km/h. The experimental data was collected and discussed. Statistical analysis was performed in terms of bottom echo drop as a function of velocity, pressing force, and film thickness between the sensor and the rail material surface, as well as the coupling fluid stream intensity. The results proved the feasibility of the device for usage at high speeds for the state monitoring of rails in service. Full article

An Ultrasonic Test (UT), a type of non-destructive test, is used to inspect the manufacturing integrity of carbon spar-caps of wind blades. When performing a UT, an intermediate medium is used to improve the signal detection ability between the inspection target and the probe. However, if the intermediate-medium residue is not removed, it acts as a contaminant in the interface between the spar-cap and the blade skin. This has a negative effect on the adhesion characteristics. A quantitative method is required for removing the intermediate medium and peel ply after the UT. After the UT, the interfacial characteristics of the spar-cap surface are examined according to the method of removing the intermediate medium in this study. The static contact angle and the work of adhesion (W_a) were measured according to various surface treatment conditions. In addition, shear strength of the Carbon Fiber-Reinforced Plastic (CFRP) spar-cap was evaluated by the lap shear test. An optimized method of peel-ply removal combined with an intermediate medium was found in this study. An optimal guideline for intermediate-medium treatment could be proposed when evaluating the manufacturing integrity of real wind blade spar-caps using UT. Full article

Conveyor belts are among the most critical components of material transport systems across various industrial sectors, including mining, energy, cement production, metallurgy, and logistics. Their reliability directly affects the continuity and operational costs. Traditional methods for assessing belt condition often require downtime, are labor-intensive, and involve a degree of subjectivity. In recent years, there has been a growing interest in non-destructive and remote diagnostic techniques that enable continuous and automated condition monitoring. This paper provides a comprehensive review of current diagnostic solutions, including machine vision systems, infrared thermography, ultrasonic and acoustic techniques, magnetic inspection methods, vibration sensors, and modern approaches based on radar and hyperspectral imaging. Particular attention is paid to the integration of measurement systems with artificial intelligence algorithms for automated damage detection, classification, and failure prediction. The advantages and limitations of each method are discussed, along with the perspectives for future development, such as digital twin concepts and predictive maintenance. The review aims to present recent trends in non-invasive diagnostics of conveyor belts using remote and non-destructive testing techniques, and to identify research directions that can enhance the reliability and efficiency of industrial transport systems. Full article

Automated ultrasonic testing (AUT) serves as a vital method for evaluating critical infrastructure in industries such as oil and gas. However, a significant challenge in deploying artificial intelligence (AI)-based interpretation methods for AUT data lies in improving their reliability and effectiveness, particularly due to the inherent scarcity of real-world defective data. This study directly addresses data scarcity in a weld defect classification task, specifically concerning the detection of lack of fusion (LOF) defects in weld inspections using a proprietary industrial ultrasonic B-scan image dataset. This paper leverages state-of-the-art generative models, including Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPM) (StyleGAN3, VQGAN with an unconditional transformer, and Stable Diffusion), to produce realistic B-scan images depicting LOF defects. The fine-tuned Transformer-based models, including ViT-Base, Swin-Tiny, and MobileViT-Small classifiers, on the regular B-scan image dataset are then applied to retain only high-confidence positive synthetic samples from each method. The impact of these synthetic images on the classification performance of a ResNet-50 model is evaluated, where it is fine-tuned with cumulative additions of synthetic images, ranging from 10 to 200 images. Its accuracy on the test set increases by 38.9% relative to the baseline with the addition of either 80 synthetic images using VQGAN with an unconditional transformer or 200 synthetic images by StyleGAN3 to the training set, and by 36.8% with the addition of 150 synthetic images by Stable Diffusion. This also outperforms Transformer-based vision models that are trained on regular training data. Concurrently, knowledge distillation experiments involve training ResNet-50 as a student model, leveraging the expertise of ViT-Base and Swin-Tiny as teacher models to demonstrate the effectiveness of adding the synthetic data to the training set, where the greatest enhancement is observed to be 34.7% relative to the baseline. This work contributes to advancing robust, AI-assisted tools for critical infrastructure inspection and offers practical pathways for enhancing available models in resource-constrained industrial environments. Full article

In coal mine production processes, conveyor belts are essential components. They play a crucial role in minimizing the risk of belt failure, enabling unmanned operations in hazardous environments, digitally monitoring production metrics, and facilitating timely information feedback, all of which are vital. This paper provides a systematic review of the fundamental concepts, operational principles, and prevalent algorithms associated with conveyor belt detection technology. It summarizes recent research advancements and current applications in key areas while outlining future trends. The paper addresses the challenges of real-time detection during highspeed operations and the identification of defects in various internal filling materials. It evaluates the feasibility of employing methods such as X-ray detection, magnetic flux leakage detection, ultrasonic detection, radio frequency detection, and terahertz wave detection for high-speed conveyor belt inspection and defect identification in filling materials. Based on a comprehensive analysis, terahertz wave detection technology demonstrates significant potential for advancement in non-destructive testing of conveyor belts, owing to its broad applicability and ability to directly identify the location and size of damage. This review aims to provide technical support for selecting testing methods for steel cord conveyor belts. Full article

In assessing the strength properties of stone materials, especially in historic structures, ultrasonic measurements are widely used as a non-destructive testing (NDT) method. Actual stone degradation in situ is estimated based on various laboratory tests which allow researchers to correlate the number of artificial ageing cycles of stone specimens with ultrasonic wave velocity measured on these specimens. This paper presents the results obtained for granite, marble, limestone, travertine and sandstone which underwent various cyclic ageing tests including freezing and thawing, high temperature and salt crystallization. Analysis of the obtained results shows that, independent of the stone type tested and independent of the ageing test applied, a rate of change in the stone elastic properties is described by an ordinary differential equation whose solution is an exponential law analogue to the Newton’s law of cooling. The degradation function model can be used for further research on expected residual strength and dynamics of the heritage materials degradation processes. Full article