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Elastodynamic metasurfaces composed of surface-mounted resonators show great promise for guided wave control in diverse applications, e.g., seismic and vibration isolation, nondestructive evaluation, or surface acoustic wave devices. In this work, we revisit the well-studied problem of "rod-shaped" resonators coupled to a plate to reveal the relationship between the resonator's resonances and antiresonances obtained under unidirectional harmonic excitation, and the resultant frequency bandgap for S 0 Lamb mode propagation once a metasurface is arranged. This relationship is shown to hold true even for non-prismatic resonators, such as those presented in our recent studies, in which we established a systematic resonator design methodology using topology optimization by matching a single resonator's antiresonance with a predefined target frequency. Our present study suggests that considering the waveguide (plate) during the resonator design is not essential and encourages a feasible resonator design approach to achieve wide bandgaps just by customizing a single resonator's resonances and antiresonances. We present a topology optimization design methodology for resonators that drive resonances away from antiresonances, i.e., a resonance gap enhancement, yielding a broadband S 0 mode bandgap while ensuring the desired bandgap formation by matching antiresonances with a target frequency. The transmission loss of metasurfaces composed with topology-optimized resonators is numerically verified, confirming the generation of wider bandgaps compared to resonators designed without resonance gap enhancement and broadening the applicability of locally resonant metasurfaces. [ABSTRACT FROM AUTHOR]

A complete simulation chain for the laser-based generation of a microfocus-size gamma ray beam of multi-MeV energy range able to produce radiographic images has been developed. The major interactions needed to obtain such a beam are treated individually. Particle-in-cell is used to study the generation of the electron beam through laser wake-field acceleration (LWFA), and Geant4 is employed for the Bremsstrahlung photon emission and for testing the imaging capabilities of the generated gamma beam. The paper presents detailed discussions about the implementation of each simulation, along with the results obtained. The structure of the article walks through the LWFA of up to 100 MeV electron beam, followed by its attenuation through a tantalum foil generating a 300 μ m spot size photon beam, later used for imaging of a thick lead test-object, assessing a 100 μ m resolution, and confirming the simulated imaging setup suitability for non-destructive testing applications of thick high-density objects. An analysis of the quality control parameters for the generated image along with discussions of possible improvements is also included. [ABSTRACT FROM AUTHOR]

Mode coupling and conversion between longitudinal and transverse modes are ubiquitous and universal in elastic waves, presenting a challenge in realistic applications such as nondestructive evaluation and geological exploration, where independent and separate manipulation of each mode is demanded. In this article, we propose a design of elastic metalens that can realize a high-efficiency focusing for the longitudinal wave and a V-shaped converging pattern for the transverse wave at the same time. The metalens is constructed from a metagrating, where each meta-atom has a simple configuration and renders high diffraction efficiency even for large steering angles, enabled by concurrent utilization of grating diffraction theory and advanced optimization algorithms. Interestingly, an arbitrary energy splitting ratio between the reflected longitudinal and transverse waves can be obtained by precisely controlling the coupling strength and conversion efficiency between them, providing improved flexibility and adaptability to various application environments. Two illustrative examples with a sharp focusing effect and tailored conversion efficiency are explicitly demonstrated, with a 50/50 energy splitting ratio between the longitudinal and transverse waves in the first case, and a 70/30 ratio in the second one. [ABSTRACT FROM AUTHOR]

The performance evaluation of terahertz measurement technology on complex curved surfaces, such as blades, holds significant importance. Industrial robots can be equipped with probes to realise this technology. However, overcoming the limitation of robot encoder modification and addressing the problem of poor pose accuracy in robot-assisted terahertz detection is one of the main challenges affecting the evaluation results . This paper proposes a compensation method combining model and optimisation-based machine learning for robot-assisted terahertz detection. First, robot trajectory planning that meets the requirements for terahertz measurement of blades is described, and a NURBS surface model of the blade and efficiently plan the probe’s orientation is proposed. Second, a rapid and precise method for optimising trajectory pose accuracy, combining the robot’s D-H model with calibration compensation based on Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM), is proposed. Third, prediction and compensation method is simulated and validated against the trajectory planning results. Finally, precision evaluation is conducted based on the calibration outcomes, and the robot assisted terahertz blade detection experiment is carried out. The results show that compared to the other compensation method, this approach improves both precision and efficiency, laying the foundation for detection devices in terahertz measurement research on curved surfaces. [ABSTRACT FROM AUTHOR]

This paper presents experimental investigations on the joints of concrete frame beams and columns with 20% coal ash slag replacing cement. The failure mechanism of concrete frame joints with coal ash slag as a cement replacement is examined, focusing on the impacts of two scenarios – concrete with 20% coal ash slag and ordinary concrete – on the failure mode and seismic performance of beam–column joints, as well as the influence of varying concrete strength grade when the coal ash slag content is 20%. The main research variables are the presence of coal ash slag in the concrete and the concrete strength grade. Findings reveal typical failure modes at the beam-end flexural plastic hinge, progressing through stages of initial cracking, yielding, limit and failure. Joints without coal ash slag exhibit 10.3–16.2% higher ductility, while those with 20% coal ash slag show a 14% improvement in bearing capacity. The C50 joint with 20% coal ash slag outperformed the C40 joint in bearing and energy dissipation, with robust hysteretic curves meeting seismic requirements. Thus, replacing 20% of cement with coal ash slag enhances the durability and seismic performance of concrete components. [ABSTRACT FROM AUTHOR]

Rolling bearings are widely used in rotating machinery, such as aero-engine spindles, flying machines, wind turbines, etc. Bearing condition monitoring is of practical importance. The acoustic emission (AE) signal has impact and rapid attenuation characteristics. Most existing research on fault diagnosis is not focused on rapid attenuation characteristics. According to this characteristic, a time-frequency coherent and time-frequency energy change rate (TFC-TFECR) method is proposed to identify the AE signals of bearing faults. This paper investigates the effect of the time-frequency coherent (TFC) method on attenuation coefficient. It also focuses on the deviation of the time-frequency energy change rate of the TFC-TFECR method, which is superior to the time-frequency energy. Feature extraction of AE signals from cylindrical roller bearings is carried out through three typical states of cylindrical roller bearings. The feature values of the TFC-TFECR method are input into the SVM model, and the sparrow search algorithm optimises the SVM model. The experimental results show that the method can effectively realise the feature extraction and state recognition of the AE signals of cylindrical roller bearings, and the accuracy rate reaches 99.3827% at 600 r/min and 98.7654% at 1200 r/min. This paper provides a new method for non-destructive testing of rotating machinery bearings. [ABSTRACT FROM AUTHOR]

Fast volumetric non-destructive testing methods are needed, especially for quality control in manufacturing lines. Ultrasonic testing with full waveform evaluation is a promising method for this. However, changes in coupling conditions or environmental factors can significantly alter the ultrasound signal, sometimes more than actual defects. This study investigates the effect of various factors on the ultrasound signal based on a Monte Carlo study with wavefield simulations. The test specimens comprise aluminium plates with holes of varying sizes and positions. Using both experimental as well as simulated data, the performance of two commonly used comparison metrics, namely the ${R^2}$ R 2 score and the magnitude-squared coherence integral, for detecting defects in manufactured parts is evaluated. It was found that the magnitude-squared coherence integral is more robust against random influences than the ${R^2}$ R 2 score. Additionally, factors influencing the entire plate exhibit the most significant impact on the signals. The hole positions and dimensions change the signals and the value of the comparison metrics significantly and are difficult to distinguish by one metric. A deep learning model, however, is capable of performing this task and it outperforms the comparison metrics in defect detection. The performance of the approaches is assessed with probability of detection curves. [ABSTRACT FROM AUTHOR]

Reliable and accurate prediction of the creep life of power plant components is crucial for both economic and safety reasons. Existing prediction models, based on creep test data, can be complex and time-consuming. Nonlinear ultrasonic (NLU) is a widely-accepted non-destructive testing (NDT) technique for evaluating damage progression in crept specimens. The information from NLU measurements alone is insufficient to forecast the life of any component. In real-time applications, intelligent NDT protocols are needed to enable fast and accurate life prediction of such components. A methodology for creep life prediction using artificial neural networks (ANN) has been introduced based on NLU test results of crept P92 steel specimens. The technique involved creep tests of P92 specimens exposed to a temperature of 625⁰C with applied stress ranging from 120MPa to 160MPa, NLU measurements at each step load, and prediction of creep life of the material with a ANN trained with creep strain and NLU test data. The technique involves prediction from previously generated historical data, thus saving both cost and time of conducting continuous experiments. This approach for ANN modeling of NLU data can be considered a reliable, time-saving, and effective technique for assessing creep damage progression in power plant components. [ABSTRACT FROM AUTHOR]

Synchrotron radiation X-ray Computed Tomography(CT) imaging is an effective means of non-destructively revealing the internal structure of samples. To obtain high-quality reconstructed slices, the number of projections should be at least ${\pi \over 2}{N_{detector}}$ π 2 N detector (${N_{detector}}$ N detector is the number of detector pixels occupied by the sample diameter). Acquiring excessive projections leads to high radiation doses and long scanning time. Insufficient projections, falling below the Nyquist sampling criterion, result in significant streaking artifacts. This paper proposes a method that fuses the structural similarity index and the peak signal-to-noise ratio as a loss function of the Attention U-Net network to correct streaking artifacts. Moreover, a new deep learning framework based on the Transformer and convolutional neural network is also proposed to solve the artifact problem. At 200 and 400 views, the two proposed methods significantly improve the reconstructed slices' quality, qualitatively and quantitatively outperforming the classical streaking artifact correction method. The optimal sparsity ratio for sparse-view CT imaging is investigated: the Trans-attunet method corrects streaking artifacts at a 1/12 sparsity ratio to visualize large sample features, whereas ratios of 1/3 or 1/2 more effectively recover fine structures. The optimal sparsity ratio is contingent upon the trade-off between desired image quality and imaging efficiency. [ABSTRACT FROM AUTHOR]

Laminated veneer lumber panels (LVL) are engineered wood products suitable for application in construction contexts. However, LVL panels have some deficient elastic properties (e.g., E 22 ) concerning other elastic properties (e.g., E 11 and G 12 ), which may cause problems in structural applications. Carbon and basalt fibers (CF and BF) are reinforcement alternatives for LVL panels, as they can be included in the interior or exterior wood veneer bonding process. This work aims to analyze the effect of incorporating CF and BF fibers in the orthotropic elastic properties of radiata pine LVL panels through a nondestructive method based on transverse vibration tests and model updating techniques. Accordingly, 20 LVL panels of 15 mm thickness were fabricated and tested with different reinforcing fibers and adhesives. Then, some relevant panels' dynamic properties were identified through experimental modal analysis. Finally, three relevant panels' orthotropic elastic properties were estimated simultaneously using finite-element model updating techniques and Python-based deterministic calibration scripts. The results suggest that the reinforced LVL panels obtained significant increases in their orthotropic elastic properties, in the order of 22%, 333%, and 27% for E 11 , E 22 , and G 12 , respectively. These results show the effectiveness of the type of reinforcement applied and the potential application of the nondestructive evaluation method in other contexts. [ABSTRACT FROM AUTHOR]

Adhesive joints are extensive used in various industrial applications. Bonded quality is crucial for ensuring structural integrity and safety. In this study, the nonlinear non-collinear wave mixing techniques were developed to nondestructive evaluate micro imperfections in adhesive joints, including adhesive degradation and bond weakening. Two testing schemes were proposed via the resonance conditions of the adhesive and substrate respectively, following the classical nonlinearity theories. Numerical simulations and experiments of non-collinear wave mixing were conducted to explore the feasibility of the proposed testing schemes for accessing two typical micro imperfections in adhesive joints. Both the simulation and experimental results demonstrate that the proposed nonlinear non-collinear wave mixing method is effective for nondestructive evaluation of the micro imperfections in adhesive joints. Moreover, the scheme via resonance conditions of adhesive exhibits a higher sensitive to the adhesive degradation, whereas the one relying on the resonance conditions of substrate exhibits a higher sensitive to bond weakening. [ABSTRACT FROM AUTHOR]

Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the 'Instance Segmentation XXL-CT Challenge of a Historic Airplane' was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods. [ABSTRACT FROM AUTHOR]

This paper reviews structural health monitoring (SHM) techniques of bridge structures based on machine learning (ML) algorithms. Regular inspections and the use of non-destructive testing are still the common damage-detection methods; however, they are susceptible to subjectivity and human error and involve prolonged duration. With emerging technologies such as artificial intelligence and the development of wireless sensors, SHM has shifted from offline model-driven damage detection to online/real-time data-driven damage detection. In this paper, both supervised and unsupervised ML algorithms are examined to determine which of the latest methods would be the most suitable and effective for the SHM of bridge structures. This review paper investigates recent studies on data acquisition, data imputation, data compression, feature extraction and pattern recognition using supervised/unsupervised ML algorithms. [ABSTRACT FROM AUTHOR]

A method utilizing vibroacoustic nondestructive testing technology was proposed to address the difficulty of detecting early internal browning in pears. It combined the S-transform signal analysis method and MobileNetV3 neural network to distinguish pears with different degrees of browning. After converting the one-dimensional response signal into a two-dimensional time–frequency image through the S transform, these time–frequency images were then categorized into healthy, slight browning, and severe browning pears based on the actual degree of browning observed in the pears. These three classes of datasets were inputted into an improved MobileNetV3 model, which incorporates a random cropping module (RC) and attention mechanism (CA). The constructed MobileNetV3-RA model achieved an overall classification accuracy of 96.79%. Specifically, the accuracy values were as follows: 97.50% for healthy pears, 93.75% for slightly browning pears, and 95.83% for severely browning pears. Notably, on the imbalanced test set, the F1, Ka, MCC, and IAM values for the MobileNetV3-RA model were 94.05%, 91.72%, 91.57%, and 83.97%, respectively. Additionally, MobileNetV3-RA has faster detection speed and fewer parameters, with respective values of 0.27s and 4.45M, compared to other common classification models. Therefore, this study provides an effective nondestructive testing method for early internal browning of pears and provides some research ideas and methods for internal quality detection of other fruits and vegetables. [ABSTRACT FROM AUTHOR]