Search Results (322)

Modern aerospace engineering places increasing emphasis on materials that combine low weight with high mechanical performance. Fiber metal laminates (FMLs), which merge metal layers with fiber-reinforced composites, meet this demand by delivering improved fatigue resistance, impact tolerance, and environmental durability, often surpassing the performance of their constituents in demanding applications. Despite these advantages, inspecting such thin, layered structures remains a significant challenge, particularly when they are difficult or impossible to access. As with any new invention, they always come with challenges. This study examines the effectiveness of the fundamental anti-symmetric Lamb wave mode (A0) in detecting weak interfacial defects within Carall laminates, a type of hybrid fiber metal laminate (FML). Delamination detectability is analyzed in terms of strong wave dispersion observed downstream of the delaminated sublayer, within a region characterized by acoustic distortion. A three-dimensional finite element (FE) model is developed to simulate mode trapping and full-wavefield local displacement. The approach is validated by reproducing experimental results reported in prior studies, including the author’s own work. Results demonstrate that the A0 mode is sensitive to delamination; however, its lateral resolution depends on local position, ply orientation, and dispersion characteristics. Accurately resolving the depth and extent of delamination remains challenging due to the redistribution of peak amplitude in the frequency domain, likely caused by interference effects in the acoustically sensitive delaminated zone. Additionally, angular scattering analysis reveals a complex wave behavior, with most of the energy concentrated along the centerline, despite transmission losses at the metal-composite interfaces in the Carall laminate. The wave interaction with the leading and trailing edges of the delaminations is strongly influenced by the complex wave interference phenomenon and acoustic mismatched regions, leading to an increase in dispersion at the sublayers. Analytical dispersion calculations clarify how wave behavior influences the detectability and resolution of delaminations, though this resolution is constrained, being most effective for weak interfaces located closer to the surface. This study offers critical insights into how the fundamental anti-symmetric Lamb wave mode (A0) interacts with delaminations in highly attenuative, multilayered environments. It also highlights the challenges in resolving the spatial extent of damage in the long-wavelength limit. The findings support the practical application of A0 Lamb waves for structural health assessment of hybrid composites, enabling defect detection at inaccessible depths. Full article

The integrity of bolted components primarily relies on the quality of interfacial contact, which is achieved by maintaining prescribed bolt torque levels. However, challenges arise from corrosion-induced bolt head damage, potentially compromising the bolt preload, and quantifying such effects remains unanswered. Many studies often compare bolt corrosion’s effects to bolt loosening as both affect the interfacial contact stresses to some extent. This technical study aimed to investigate whether a correlation exists between the impact of bolt head damage and the different levels of bolt torque. Guided wave ultrasonic testing (UT) was implemented for this investigation. Laboratory experiments were conducted to monitor the transmission of ultrasonic signals across the bolted interface first during the bolt-tightening process. Once the highest bolt torque was achieved, the process was repeated for a simplified corrosion scenario, simulated by artificially damaging the bolt head in a controlled manner. The analysis focused on studying the transmission of signal energy for both scenarios. The findings revealed different trends for the signal energy transmission during bolt tightening, which are subjective to the inspection frequency. On the contrary, even at an advanced level of bolt head damage corresponding to 16% mass loss, no clear or monotonic trend was observed in the total transmitted energy. While the total energy remained relatively stable across all inspection frequencies, distinct waveform changes, such as energy redistribution and the emergence of additional wave packets, were observed. The findings emphasize the need for more advanced waveform-based analysis techniques to detect and interpret subtle changes caused by bolt degradation. Full article

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

The timely detection of delamination is essential for preventing catastrophic failures and extending the service life of carbon fiber-reinforced polymers (CFRP). Full wavefields in CFRP encapsulate extensive information on the interaction between guided waves and structural damage, making them a widely utilized tool for damage mapping. However, due to the multimodal and dispersive nature of guided waves, interpreting full wavefields remains a significant challenge. This study proposes an end-to-end delamination imaging approach based on UNet++ using 2D frequency domain spectra (FDS) derived from full wavefield data. The proposed method is validated through a self-constructed simulation dataset, experimental data collected using Scanning Laser Doppler Vibrometry, and a publicly available dataset created by Kudela and Ijjeh. The results on the simulated data show that UNet++, trained with multi-frequency FDS, can accurately predict the location, shape, and size of delamination while effectively handling frequency offsets and noise interference in the input FDS. Experimental results further indicate that the model, trained exclusively on simulated data, can be directly applied to real-world scenarios, delivering artifact-free delamination imaging. Full article

Compared to traditional temperature measurement methods, ultrasonic temperature measurement technology based on the principle of resonance offers advantages such as shorter section lengths, higher signal amplitude, and reduced signal attenuation. First, the type of sensor-sensitive element was determined, with a resonant design chosen to improve measurement performance; using magnetostrictive and resonant temperature measurement principles, the length, diameter, and resonator dimensions of the waveguide rod were designed, and a molybdenum–rhenium alloy (Mo-5%Re) material suitable for high-temperature environments was selected; COMSOL finite element simulation was used to simulate the propagation characteristics of acoustic signals in the waveguide rod, observing the distribution of sound pressure and energy attenuation, verifying the applicability of the model in high-temperature testing environments. Second, a resonant temperature sensor consistent with the simulation parameters was prepared using a molybdenum–rhenium alloy waveguide rod, and an ultrasonic resonant temperature-sensing system suitable for high-temperature environments up to 1800 °C was constructed using the molybdenum–rhenium alloy waveguide rod. The experiment used a tungsten–rhenium calibration furnace to perform static calibration of the sensor. The temperature range was set from room temperature to 1800 °C, with the temperature increased by 100 °C at a time, and it was maintained at each temperature point for 5 to 10 min to ensure thermal stability. This was conducted to verify the performance of the sensor and obtain the functional relationship between temperature and resonance frequency. Experimental results show that during the heating process, the average resonance frequency of the sensor decreased from 341.8 kHz to 310.37 kHz, with an average sensitivity of 17.66 Hz/°C. During the cooling process, the frequency increased from 309 kHz to 341.8 kHz, with an average sensitivity of 18.43 Hz/°C. After cooling to room temperature, the sensor’s resonant frequency returned to its initial value of 341.8 kHz, demonstrating excellent repeatability and thermal stability. This provides a reliable technical foundation for its application in actual high-temperature environments. Full article

The ultrasonic guided wave-based damage imaging methods are often limited in detection accuracy and resolution due to the dispersive characteristics of guided waves. Finding ways to extract more information from the guided wave field to improve imaging resolution has always been a hot topic in ultrasonic imaging. Based on the same set of guided wave field data obtained by numerical simulation and experiments, this paper compares the detection accuracy and resolution of the time-domain delay-and-sum (DAS) method, the frequency-domain DAS method, and the DORT-MUSIC method, which integrates time-reversal operator decomposition with multiple signal classification. The results show that, compared to the traditional time-domain imaging method, the frequency-domain method that incorporates dispersion relations exhibits significantly higher imaging accuracy. Additionally, the DORT-MUSIC method demonstrates a remarkable advantage in resolution, which can approach the diffraction limit. Related work in this paper provides a research basis for improving the imaging accuracy and resolution for ultrasonic guided waves in the practical application of structure damage detection. Full article

Lamb waves offer a series of desirable features for Structural Health Monitoring (SHM) applications, such as the ability to detect small defects, allowing to detect damage at early stages of its evolution. On the downside, their propagation through media with multiple geometrical features results in complicated patterns, which complicate the task of damage detection, thus hindering the realization of their full potential. This is exacerbated by the fact that numerical models for Lamb waves, which could aid in both the prediction and interpretation of such patterns, are computationally expensive. The present paper provides a flexible surrogate to rapidly evaluate the sensor response in scenarios where Lamb waves propagate in plates that include multiple features or defects. To this end, an offline–online ray tracing approach is combined with Frequency Response Functions (FRFs) and transmissibility functions. Each ray is thereby represented either by a parametrized FRFs, if the origin of the ray lies in the actuator, or by a parametrized transmissibility function, if the origin of the ray lies in a feature. By exploiting the mechanical properties of propagating waves, it is possible to minimize the number of training simulations needed for the surrogate, thus avoiding the repeated evaluation of large models. The efficiency of the surrogate is demonstrated numerically, through an example, including different types of features, in particular through holes and notches, which result in both reflection and conversion of incident waves. For most sensor locations, the surrogate achieves an error between 1% and 4%, while providing a computational speedup of three to four orders of magnitude. Full article

The inspection of corrosion and pitting-type defects is critical in the petrochemical, marine, and offshore industries. Guided wave inspection is widely used to detect these flaws and control operational costs. Higher order modes cluster (HOMC) guided waves, composed of higher-order Lamb wave modes, offer enhanced resolution compared to low-frequency guided waves. They exhibit minimal dispersion, reduced sensitivity to surface features such as T-joints, and retain most of their energy upon interacting with surface defects. This study employs two-dimensional finite element simulations to investigate the propagation and interaction of HOMC guided waves with defects in a T-joint and an aluminum plate. Both conventional fitting methods and machine learning (ML) models are used to estimate the depth of sharp defects reaching up to half the plate thickness. The results demonstrate that both approaches can utilize data from defects of one width to predict the depth of defects with a different width. The ML model outperforms the fitting method, achieving higher prediction accuracy while reducing dependence on expert knowledge. The developed method shows strong potential for characterizing sharp defects of varying widths, closely resembling real-world pitting corrosion scenarios. Full article

Ultrasonic guided waves can be employed for in-service defect detection in storage tank bottom plates; however, conventional single-array approaches face challenges from boundary scattering noise at side connection welds. This study proposes a collaborative bottom plate-wall plate detection methodology to address these limitations. Sensor arrays were strategically deployed on both the bottom plate and wall plate, achieving multidimensional signal acquisition through bottom plate array excitation and dual-array reception from both the bottom plate and tank wall. A correlation coefficient-based matching algorithm was developed to distinguish damage echoes from weld-induced scattering noise by exploiting path-dependent signal variations between the two arrays. The investigation revealed that guided wave signals processed through data matching effectively preserved damage echo signals while substantially attenuating boundary scattering signals. Building upon these findings, correlation matching was implemented on guided wave signals received by corresponding array elements from both the bottom plate and wall plate, followed by total focusing imaging (TFM) using the processed signals. Results demonstrate that the collaborative bottom plate-wall plate detection imaging cloud maps, after implementing signal correlation matching, effectively suppress artifacts compared with imaging results obtained solely from bottom plate arrays. The maximum relative localization error was measured as 5.4%, indicating superior detection accuracy. Full article

In this paper, we introduce an advanced AI-based solution for predicting structural damage in aircraft laminates. Our innovative approach focuses on detecting and locating fatigue damage within composite structures, thereby enhancing the assessment of aircraft health and usage. By leveraging state-of-the-art ultrasonic guided wave (UGW) inspection simulations of composite laminates integrated with piezoelectric transducers, comprehensive datasets are extracted efficiently. The signals captured by the piezoelectric sensors are utilized to engineer key features sensitive to composite structural damage, which are then used to train a deep neural network (DNN) for accurate structural damage prediction. Our findings demonstrate the significant potential of combining advanced simulation techniques with machine learning to improve the accuracy and reliability of structural health monitoring in aerospace applications. Full article

High-temperature pipelines, as core facilities in the fields of petrochemical and power, are constantly exposed to extreme working conditions ranging from 450 to 600 °C, facing risks of stress corrosion, creep damage, and other defects. Traditional shutdown inspections are time-consuming and costly. Meanwhile, existing electromagnetic acoustic transducers (EMATs) are restricted by their high-temperature tolerance (≤500 °C) and short-term stability (effective working duration < 5 min). This paper proposes a high-frequency circumferential guided wave (CLamb wave) EMAT based on a Halbach permanent magnet array. Through magnetic circuit optimization (Halbach array) and multi-layer insulation design, it enables continuous and stable detection on the surface of 600 °C pipelines for 10 min. The simulations revealed that the Halbach array increased the magnetic flux density by 1.4 times and the total displacement amplitude by 2 times at a magnet’s large lift-off (9 mm). The experimental results show that the internal temperature of the sensor remained stable below 167 °C at 600 °C. It was capable of detecting the smallest defect of a φ3 mm half-hole (depth half of the wall thickness), with a signal attenuation rate of only 0.32%/min. The signal amplitude of Q235 pipelines under high-temperature short-term detection (<5 min) was 1.5 times higher than that at room temperature. However, material degradation under high temperature led to insufficient long-term stability. This study breaks through the bottleneck of long-term detection of high-temperature EMATs, providing a new scheme for efficient online detection of high-temperature pipelines. Full article

In the field of nondestructive evaluation (NDE) using ultrasonic guided waves, accurately assessing the aging failure of insulation coatings remains a challenging and prominent research topic. While the application of ultrasonic guided waves in material testing has been extensively explored in the existing literature, there is still a significant gap in quantitatively evaluating the aging failure of insulation coatings. This study innovatively proposes an NDE method for assessing insulation coating aging failure by integrating signal processing and machine learning technologies, thereby effectively addressing both theoretical and practical gaps in this domain. The proposed method not only enhances the accuracy of detecting insulation coating aging failure but also introduces new approaches to non-destructive testing technology in related fields. To achieve this, an accelerated aging experiment was conducted to construct a cable database encompassing various degrees of damage. The effects of aging time, temperature, mechanical stress, and preset defects on coating degradation were systematically investigated. Experimental results indicate that aging time exhibits a three-stage nonlinear evolution pattern, with 50 days marking the critical inflection point for damage accumulation. Temperature significantly influences coating damage, with 130 °C identified as the critical threshold for performance mutation. Aging at 160 °C for 100 days conforms to the time-temperature superposition principle. Additionally, mechanical stress concentration accelerates coating failure when the bending angle is ≥90°. Among preset defects, cut defects were most destructive, increasing crack density by 5.8 times compared to defect-free samples and reducing cable life to 40% of its original value. This study employs Hilbert–Huang Transform (HHT) for noise reduction in ultrasonic guided wave signals. Compared to Fast Fourier Transform (FFT), HHT demonstrates superior performance in feature extraction from ultrasonic guided wave signals. By combining HHT with machine learning techniques, we developed a hybrid prediction model—HHT-LightGBM-PSO-SVM. The model achieved prediction accuracies of 94.05% on the training set and 88.36% on the test set, significantly outperforming models constructed with unclassified data. The LightGBM classification model exhibited the highest classification accuracy and AUC value (0.94), highlighting its effectiveness in predicting coating aging damage. This research not only improves the accuracy of detecting insulation coating aging failure but also provides a novel technical means for aviation cable health monitoring. Furthermore, it offers theoretical support and practical references for nondestructive testing and life prediction of complex systems. Future studies will focus on optimizing model parameters, incorporating additional environmental factors such as humidity and vibration to enhance prediction accuracy, and exploring lightweight algorithms for real-time monitoring. Full article

The diagnosis of post-stressed anchor rods is essential for maintaining the service and ensuring the safety of Electricité de France (EDF) structures. These rods are critical for the mechanical strength of structures and electromechanical components. Currently, the standard method for estimating the effective tension of post-stressed tie rods with a free length involves measuring the residual force using a hydraulic jack. However, this method can be costly, impact the structure’s operation, and pose risks to employees. Until now, there has been no reliable on-field approach to estimating residual tension using a lightweight setup. This research introduces a nondestructive method using multimodal ultrasonic guided waves to evaluate the residual tension of anchor rods with a few centimeters free at one end. The methodology was developed through both laboratory experiments and simulations. This new method allows for the extraction of dispersion curves for the first three modes, bending, torsional, and longitudinal, using time–frequency analysis and enables the estimation of the steel bar’s properties. Future work will focus on applying this methodology in the field. Full article

This paper presents an experimental study of wave motions and a global diagnostics method in a realistic aerospace-grade composite component with a complex design. Due to the frequency dependence of the velocity, wave propagation in anisotropic materials is difficult to describe quantitatively. The analysis of experimental ultrasonic wave propagation and the interactions with discontinuities in thin-walled aircraft structures can provide a plethora of information on the wave structure, the mode shapes, and stiffness reduction. An experiment is devised with a network of various omnidirectional sensor configurations to activate and measure structural responses. The measurement process can be leveraged for flaw detection in large multilayered structures. Physically, this corresponds to analyzing the dispersive behavior and scattering properties of ultrasonic waves, the shape of the waveforms, and their corresponding velocities. Ultrasonic waves are measured in a realistic CFRP fuselage panel in a pristine state and after impacts at different energy levels. Simulations do not allow the wave motion in complex and large design structures to be entirely comprehended. The sensitivity of the guided waves as a damage detection tool is proved for the fuselage structure by an extensive measurement campaign and a probabilistic imaging approach based on health indicator fusion. Full article

In this study, a detection method utilizing nonlinear ultrasonic guided waves is presented to tackle the difficulties in detecting localized damage and weakening in bonded composite structures. For a three-layer structure made of polystyrene, acrylic resin, and aluminum plate, dispersion equations for ultrasonic guided waves were developed using the spring model and wave equation. The A1-S1 mode was selected by examining the material parameters’ influence on the adhesive layer’s dispersion curves. The finite element method was employed to simulate the propagation characteristics of ultrasonic guided waves within the composite structure. The error between the theoretically calculated and simulated group velocities was less than 5.15%. As the propagation distance increased, both the nonlinearity coefficient and the amplitude of the second-order harmonic showed an upward trend. This indicates a significant accumulation effect at the second harmonic of nonlinear guided waves. Compared to without adhesive layer weakening, localized and overall weakening resulted in higher amplitudes of the second-order harmonic. Experimental testing of ultrasonic guided waves was conducted to investigate the nonlinear properties of the composite structure. The error between the experimentally measured and theoretically calculated group velocities was less than 6.96%. The experimental results corroborated the propagation accumulation effect of the second-order harmonic amplitude. Full article