In power ultrasonics, the Langevin ultrasonic transducer has been widely utilised across medical and industrial applications, including for bone surgery, food cutting, and cavitation generation. Transducers for these applications are typically tuned to a fundamental operating mode, often the first longitudinal, for optimal interaction with a target material or structure. Currently, there is a growing interest in ultrasonic devices with tuneable dynamic properties, including resonance frequency, for optimising performance in these applications. To overcome limited frequency tuning capabilities of current configurations, this study demonstrates a Langevin transducer which is designed and fabricated incorporating the shape memory alloy Nitinol as its end masses. The rationale is that the change in elastic properties of these end masses with temperature will induce a change in the fundamental resonance frequency of the transducer, thereby demonstrating a viable and novel approach to controlling resonance frequency. Laser Doppler Vibrometry was used to characterise the first and third longitudinal modes at room temperature, correlating closely with finite element analysis results. Harmonic analysis was then conducted at various environmental temperatures to show changes in the resonance frequencies and vibration amplitudes of both modes as functions of temperature. The tuneable resonance of the Nitinol Langevin transducer (NLT) has a dependency on changes in the thermomechanical properties of Nitinol from its martensitic phase transformation, demonstrated through structural design factors. The transducer exhibits maximum resonance frequency increases of above 15 % and 10 % for the L1 and L3 modes respectively, between 30 °C and 100 °C. This research enables a new generation of Langevin ultrasonic transducers fabricated using advanced materials for multifrequency and tuneable resonance applications.

Ultrasonic characterization of polycrystalline materials is traditionally based on a single-exponential two-point correlation function (TPCF). However, industrial metallic polycrystals often exhibit a wide grain size distribution, for which the classical analytical scattering-induced attenuation frameworks fail to reproduce the experimental measurements. In this paper, we introduce a new closed-form TPCF that embeds the full volumetric grain size distribution through an analytic convolution of spherical grain statistics. The resulting expression naturally reduces to the classical spherical TPCF when the distribution width tends to zero. Coupling this TPCF with Weaver's framework for elastic wave attenuation produces frequency-dependent attenuation formulas that depend on the first two moments of the grain size distribution. To evaluate the robustness of the proposed model, we generated synthetic aluminum microstructures that span a wide range of coefficients of variation of grain sizes. TPCFs measured from Laguerre-Voronoi tessellation-based microstructures closely match our predictions across a wide range of coefficients of variations (CVs) from moderate to large, whereas other models systematically misestimate the correlation for large CVs. By conducting a series of comparisons with semi-analytical, numerical, and experimental attenuation coefficients reported in the literature, we show the robustness of our model. The proposed formulation, therefore, extends ultrasonic scattering theory to polycrystals with realistically broad grain size distributions, supplying a physically interpretable bridge between measurable grain statistics and macroscopic wave attenuation. This advance opens the door to nondestructive, distribution-aware inversion of microstructural parameters in polycrystalline materials.

The dynamics of rising bubbles in water under a transversely oriented ultrasonic standing wave field was experimentally investigated. The acoustic field distribution was characterized using scanning focused laser differential interferometry (SFLDI). The bubbles' trajectories were captured by high-speed imaging, from which velocities and accelerations were calculated. High-magnification particle image velocimetry (PIV) was employed to examine the surrounding flow field in detail. Results showed that bubbles in the presence of ultrasound (BPU) exhibited a 25%-56% velocity reduction compared to bubbles in the absence of ultrasound (BAU). Released from the tank bottom, BPU were immediately drawn by the transverse Bjerknes force and ascended along the nodal region of the standing waves. As BPU accelerated, they experienced a significant quasi-periodical deceleration-acceleration motion. PIV analysis revealed that this phenomenon was strongly correlated with increased boundary layer shear and enhanced vortex shedding, resulting in severe viscous dissipation induced by the acoustic field. These findings provided new insights for bubble manipulation in ultrasonic-assisted chemical engineering, mineral processing, and biomedical applications.

The composite wing leading edge (CWLE) is a critical component that determines the aerodynamic characteristics of airplanes operating in harsh environments. A large CWLE, composed of multiple material layers in a curved, sandwiched structure with varying thicknesses, is prone to debonding during both manufacturing and service. Conventional ultrasonic C-scans using bulk waves require extensive time for offline testing, making it essential to develop an efficient debonding evaluation method suitable for CWLE. This study implemented a pair of PZT arrays, functioning as excitation and reception arrays, in a section of CWLE at equal intervals to create sparse regions. A novel sparse spatial-partition probabilistic imaging method was proposed to localize debonding based on changes in ultrasonic guided waves, offering advantages of speed, baseline-free operation, and multi-feature fusion. Performance disparities among PZTs due to manufacturing and installation were addressed through comparison and compensation. The damage index of the guided wave was calculated by combining amplitude and arrival time, and the weight distribution of the sensing path, with different shape factors, was multiplied by the damage index to determine the regional probability. After determining the region with the highest probability, the probabilities on the paths between the PZT within this region and its opposing PZTs were given more weights, ultimately yielding a probability image that clearly pinpoints the debonding location. The debonding results were validated through CT scanning with a localization error of 5.02 mm. A comparison with conventional guided wave-based localization methods demonstrates that the proposed method achieves superior damage localization accuracy while maintaining equally high efficiency. The method is also validated under different measurement areas within a larger scale CWLE, showing the similar root mean square errors as that from the small scale CWLE. It suggests that the proposed method offers an efficient and accurate approach to debonding evaluation in complex material structures such as CWLE.

Reliable detection of critical defects in thick Carbon Fiber Reinforced Polymers, particularly delamination, is a significant challenge. This task becomes severely complicated when complex microstructures such as fiber waviness limit the effectiveness of conventional ultrasonic testing. To address this, a dual-stream deep learning framework with an efficient and interpretable AttentionFusion module is proposed, which synergistically integrates spatial-morphological information from B-scan images with physics-rich, multi-angle scattering signatures from raw Full Matrix Capture data. Through the adaptive weighing of both static B-scan and dynamic multi-angle inspection streams, the most salient features are leveraged by a YOLOv8-based detector for defect identification. When validated on a dataset consisting of 2776 samples, a 25.8% relative mAP50 improvement over a single-stream baseline was achieved, with this margin increasing to 29.9% on challenging wavy-fiber samples. The critical contribution of the AttentionFusion mechanism was confirmed via ablation studies. Furthermore, the framework's decision-making process was elucidated through visualization of attention maps, enhancing its transparency. By leveraging raw Full Matrix Capture data often discarded in traditional pipelines, a more accurate and trustworthy solution for automated nondestructive testing in complex aerospace composites is provided.

Under severe cooling conditions, machining SiC/SiC composites leads to pronounced tool wear, compromising machining efficiency. While ultrasonic-assisted grinding (UAG) demonstrates potential for enhancing surface quality, the wear characteristics of grinding tools in this process remain insufficiently characterized. This study employs tribological and kinematic analysis coupled with experimental verification to elucidate wear mechanisms. Results identify four principal wear modes: abrasive wear, fracture wear, grain detachment, and wheel clogging, involving both two-body and three-body wear. High-frequency ultrasonic vibrations induce surface microfracture of abrasive particles while sinusoidal scratch patterns form on the bond. Ultrasonic vibration substantially reduces debris adhesion, minimizing wheel clogging and extending tool life, thereby enhancing grinding performance. These findings facilitate parameter optimization for UAG in high-performance ceramic matrix composites.

We proposed an on-axis, multi-bottle beam by superposing two coaxial acoustic Bessel beams to produce acoustical particle conveyors. The spatial acoustic pressure distribution and source phase, determined solely by lateral wavenumber k and the radius of the circular source, were derived to establish a theoretical basis for precise ultrasound field control. These conveyors exploit strong-gradient acoustic radiation forces at acoustic bottles, enabling stable trapping of micrometer-scale Rayleigh particles in free space. By fine-tuning the incident acoustic frequency, our acoustic tweezers can trap, push, and pull multiple particles along the propagation axis. We discussed the optimization of k-dependent phase lens, focusing on the selection of k values and the number of simultaneously emitting Bessel beams, which improves field observation and enhances trapping stability. The system employs only a single acoustic source and a phase lens, offering potential for cell manipulation and targeted medical treatments in vivo.

Multi-mode damage coupling in composite structures is a key factor preventing accurate classification of different damage types. To address this, this paper presents a damage classification framework for composite structures based on acoustic emission (AE) signal decomposition. The approach begins by generating a Peak Frequency-Normalized Count Spectrum using Pearson correlation, principal component analysis, and hierarchical clustering. This spectrum, combined with electron microscopy observations, allows for quick identification of damage types and their frequency ranges, even with limited understanding of damage mechanisms. A customized wavelet packet decomposition filter is then created to decompose AE signals, enabling precise classification of different damage types. To validate the method, multiple tensile tests on adhesive composite joints were conducted, and the AE data were classified using both the proposed method and the K-means method. The results show that, compared to the K-means method, the energy proportions of the three types of damage classified by our method consistently remain in the range of 30%-40%, with the normalized energy proportion of adhesive debonding reaching or even exceeding 50%. Our method more accurately reflects the true damage state of the specimens. It effectively mitigates the negative impact caused by the coupling of multiple damage modes, providing a new perspective for health monitoring of composite structures.

Nonlinear ultrasonic testing based on second harmonic generation has shown promise for early-stage creep damage detection. However, its practical application is constrained by a strong dependence on mode-matching conditions and signal degradation at advanced damage stages, limiting its effectiveness in complex service environments. Additionally, traditional approaches struggle to reliably characterize microstructural evolution throughout the entire creep process, affecting the accuracy of damage evaluation. To overcome these challenges, this study introduces the static component signal (β) of guided wave propagation into the creep damage assessment of superalloys. This approach broadens the characterization scope of nonlinear ultrasonic responses and enhances detection stability during later creep stages. Experimental results demonstrate that the static component is largely insensitive to mode-matching conditions, with its nonlinear parameter exhibiting a stable, linear increase throughout the creep lifetime. Compared to the second harmonic parameter-which typically exhibits a nonlinear "rise-then-fall" trend-the static component shows improved robustness and practical applicability. This method effectively addresses the limitations of conventional nonlinear ultrasonic techniques for late-stage creep damage detection, offering a valuable complementary tool for structural health monitoring and life assessment of high-temperature materials.

A crack was introduced into a glass plate by applying thermal stress, and the propagation characteristics of longitudinal ultrasonic waves at this crack were observed using the photoelastic method. Waves incident on the closed crack passed through completely, and no flaw echo was observed on an A-scope display. Propagating waves with slightly open cracks were observed using a sensitive tint technique. The results indicate that the tensile phase of these waves was reflected at the crack, whereas the compressive phase was transmitted. This phenomenon is considered the principle behind the generation of harmonic waves from a crack by contact acoustic nonlinearity. Multi-cycle ultrasonic waves were visualized, and frequency analyses were performed based on the luminance distribution. Immediately after passing through the crack, a wave component with half the incident wave frequency was observed.

Mechanical properties are critical parameters of ferromagnetic materials and directly affect their structural reliability and functional stability. Ultrasonic characterization techniques based on the magnetostrictive effect have the advantages of non-contact and high sensitivity. However, the relationship between magnetoacoustic conversion efficiency (MCE) and mechanical properties lacks sufficient theoretical support and the intrinsic mechanism remains unclear. To provide theoretical support for this, a theoretical model of magnetostrictive magnetoacoustic conversion with different mechanical parameters was constructed in this study and the key factors affecting MCE were analyzed in detail for the first time. On this basis, a non-destructive characterization method of evaluating multiple mechanical parameters was developed. Finally, experimental validation was conducted with heat-treated 3Cr13 steel samples. Both theoretical and experimental results showed a significant linear correlation between the mechanical parameters and SH wave MCE curves measured with magnetostrictive transducers. The observed experimental phenomena were consistent with the predicted patterns from the model. This study enriched the magnetoacoustic conversion theory of magnetostrictive ultrasonic transducers and provided new insights into the non-contact and non-destructive characterization of mechanical properties.

Accurate measurement of tissue mechanical properties is crucial for diagnosing and characterising various pathological conditions. Among these properties, shear wave speed (SWS) is an indicator of tissue stiffness and has been widely studied across multiple imaging modalities. Despite its advantages, the quantification of SWS is subject to various sources of uncertainty, which can impact its clinical and research applications. The uncertainty of SWS measurements based on acoustic radiation force impulse (ARFI) technology implemented using a research ultrasound system is investigated in this study. A linear array ultrasound transducer with 128 elements and a transmit frequency of 5.2 MHz was employed for both pushing and tracking. The contributions of different factors, including the effects of displacement estimator, speckle and temperature variation, to the overall measurement uncertainty were assessed using tissue-mimicking phantoms. The compiled uncertainty budget provided an expanded uncertainty of 8 % for the identified SWS, offering an in-depth understanding of the systematic effects influencing SWS and Young's modulus measurements in ultrasound elastography. The findings in this study aim to enhance the reliability of ultrasound elastography as a diagnostic tool and to provide a foundation for future studies.

The zero-group-velocity (ZGV) mode of Lamb waves exhibits unique characteristics, where acoustic energy is trapped within localized regions of the waveguide. Previous research has established that ZGV combined harmonics - generated through the nonlinear interaction of frequency mixing response (FMR) - serve as highly sensitive tools for probing local material nonlinearity. In this study, we present a modeling and numerical analysis of ZGV combined harmonics produced by the mixing of two counter-directional Lamb waves in an adhesively bonded plate, explicitly considering the influence of interfacial properties on FMR efficiency. Based on theoretical analysis, a specific Lamb wave mode triplet is selected to ensure satisfaction of the internal resonance condition. The generation of ZGV combined harmonics at the sum frequency, arising from the interaction of counter-propagating Lamb waves within the plate, is systematically modeled. The results indicate that the efficiency of combined-harmonic generation for sensitive response correlates with the acoustic energy trapping characteristics of ZGV modes. Critically, the spatial location accuracy of the wave mixing phenomenon depends on the central position of the interaction zone rather than its length. Thus, there is no requirement to optimize the mixing zone length for both spatial resolution and signal clarity simultaneously; this inherent balance enhances the applicability of FMR-based nonlinear methods. Finite element (FE) simulations demonstrated that localized interfacial degradation can be detected and characterized by scanning the wave mixing zone of the two primary Lamb waves. The numerical analysis further validated the method's capability to identify multiple localized degradations with varying severity and length in bonded structures. This work elucidates the physical mechanisms underlying ZGV combined-harmonic generation in adhesively bonded plates and presents a promising approach for non-destructive assessment of interfacial integrity via counter-directional Lamb wave mixing.

Transcranial focused ultrasound enables remote targeted therapies that were previously only possible using surgical approaches. Mechanical therapies are particularly attractive due to their confined action and the elimination of the potentially harmful tissue and skull heating. However, systems for controlled mechanical therapies of the brain have been missing. Here, we have developed a prototype of such a system. The system operates at a relatively low frequency of 325 kHz (bandwidth 270-380 kHz) to accentuate mechanical effects and minimize the shift of the focal point, field distortion, and acoustic attenuation. We evaluated the transcranial performance of the system through 21 ex-vivo human skulls. There was a favorably low shift of the focal point (mean of 1.2 mm; 2.6 mm max), a minimal increase in focal volume (mean increase of 18%), and moderate attenuation of the pressure field (average 67% pressure attenuation). These values were achieved without phase correction. These results demonstrate that systems operating at a relatively low frequency are less prone to the aberrations of ultrasound by the skull, and provide a prototype that has the potential to be used for combined neuromodulation and mechanical therapies. However, translation to clinical high-intensity applications will require further validation, including in-vivo thermometry and safety testing.

Acoustic angiography is a superharmonic contrast-enhanced ultrasound modality that maps 3-D microvasculature with fine spatial resolutions and has demonstrated potential to improve disease detection. However, the application of acoustic angiography for cancer detection currently faces challenges. Quantitative analysis relies on time-consuming, manual segmentation of individual vessels, and inter-operator variability limits reader-based discrimination. This feasibility study aims to address the limitations of current approaches with deep learning for efficient and accurate detection of tumor-associated vasculature in vivo and to validate against quantitative methods that evaluate vascular morphology. Convolutional neural networks (CNNs), namely EfficientNet, ResNet, and DenseNet, were trained on a newly collected dataset of acoustic angiography volumes (n = 195 with 98 controls and 97 tumors) in rodents using a nested cross-validation study. The best performing model, 3-D EfficientNet-B0, achieved a mean classification accuracy of 0.928 ± 0.034 with high sensitivity and specificity, comparable to previously published results. Comparison with quantitative methods in tumor cases showed correlation between high network attention regions and morphological features typically associated with malignant vessels, including increased density and tortuosity. These results highlight the efficiency and accuracy of end-to-end CNNs for tumor detection in acoustic angiography volumes, validated by known markers of malignancy.

Developing a numerical model that accurately predicts ultrasonic signals in cylinders without relying on the finite element (FE) method can significantly improve computational efficiency. However, existing models capable of predicting received ultrasonic signals by electromagnetic acoustic transducers (EMATs) in cylindrical structures remain limited. To address this gap, this paper presents a theoretical model for predicting ultrasonic signals in finite-length cylinders excited by EMATs. The model comprehensively incorporates the entire EMAT operation process, including the excitation, propagation, and reception of ultrasonic waves. Analytical expressions of the trailing pulses are first derived based on the Pochhammer-Chree theory, revealing that these pulses originate from the superposition of guided waves. Subsequently, a numerical model is developed to calculate the time-domain signals received by EMATs through modal analysis. The effectiveness and accuracy of the proposed model are validated through comparisons with FE simulations and experimental results. The findings demonstrate that the model can accurately predict the ultrasonic wave modes and key signal characteristics, including waveform, amplitude, and trailing-wave periodicity, under varying EMAT parameters. This study provides a fast and accurate approach for predicting and interpreting ultrasonic responses generated and received by EMATs in cylindrical structures.

Traditional zoom lenses employ multiple solid lens elements and electromagnetic driving mechanisms. This results in complex structures and large sizes. These limitations significantly restrict their application in compact optical systems where miniaturization is critical. To address this issue, this paper presents a flexible zoom lens module. This module is driven by a cylindrical ultrasonic motor (CYUSM). The CYUSM comprises a stator, a hollow mover, and piezoelectric ceramic (PZT) elements. It acts as a direct-drive actuator to deform a transparent elastomeric lens axially. Polydimethylsiloxane (PDMS) was selected as the optical lens material. The precise linear motion of the CYUSM dynamically controls its surface curvature. This enables continuous adjustment of the focal length. We optimized the structural parameters of the CYUSM stator and the PDMS lens using ANSYS finite element analysis. This optimization aimed to achieve modal frequency degeneracy and high electromechanical coupling efficiency. Experimental characterization of the prototype demonstrated that the CYUSM could deliver a maximum output velocity of 1.21 mm/s and a thrust force of 5.4 N (under 43.3 kHz, 200 Vp). The optical performance was evaluated using ZEMAX. The results indicated a minimum focal length of 36.5 mm for the lens module. The experimentally measured focal length trend showed high consistency with the simulation results, thereby validating the design accuracy. The module employs a coaxial hollow structure to integrate the actuator and optical path, resulting in high integration, miniaturization, self-locking, and electromagnetic interference immunity.

A novel approach based on low sampling rate data is proposed to detect early-stage bolt looseness in structural joints. This study investigates how bolt loosening affects the acoustic emission signal in a multi-bolt connection using low sampling rates. Utilizing a low sampling rate sensor enables continuous and cost-effective structural health monitoring. To validate the method, an experimental set-up was conducted on carbon steel plates fastened with M8 bolts. The proposed technique consists of two main stages. First, the effect of bolt loosening in a single-bolt joint on acoustic signals is analyzed. Second, various bolt loosening configurations are examined in a linear three-bolt setup. The influence of different permutations of bolt looseness in the linear arrangement on the final results is also discussed. The results indicate that even in the presence of fully tightened bolts capable of transmitting stress waves, the initiation of loosening can be successfully detected using time-frequency domain features combined with support vector machine (SVM) classification. Experimental results demonstrate that the proposed method achieves an accuracy of 97.53 % in detecting early-stage bolt looseness. The findings highlight the method's potential as a practical and scalable solution for improving the safety and reliability of bolted connections in industrial applications.

This paper presents a pulse-echo sound speed estimation method for layered media, utilizing prior-constrained coherent analysis. The proposed method addresses the instability in local sound speed estimation caused by phase ambiguities resulting from suboptimal probe configurations. By introducing biologically reasonable sound speed boundary constraints to compensate for errors in average sound speed (ASS) estimation, and integrating sparse interface regularization inversion models, this method suppresses noise amplification during inversion, thereby enhancing robustness. The experimental results demonstrate that using this method significantly improves performance in simulations and in vitro data, reducing the root-mean-square error (RMSE) by 68% compared to existing methods. In in vivo experiments, the average sound speed in the tested regions deviated less than 0.6% from the reference values, while maintaining high repeatability. Furthermore, ablation studies validate the synergistic effect of prior compensation and sparse regularization, confirming their effectiveness in reducing phase sensitivity and enhancing the resolution of stratified structures. This method provides a reliable quantitative sound speed assessment tool for clinical scenarios such as hepatic steatosis, simultaneously relaxing hardware requirements for ultrasound probe parameters.

This study presents a proof-of-concept miniaturized transient elastography (TE) framework for measuring myocardial elasticity during catheter-based cardiac procedures. Recognizing that mechanical properties of myocardial tissue, particularly the shear modulus, offer valuable insight into the development and progression of cardiovascular conditions such as heart failure, we propose a TE system that can be integrated into existing intracardiac catheters. A miniature (2 mm × 2 mm) piezoelectric actuator was used to generate longitudinal shear waves (LSWs) in tissue-mimicking phantoms with varying shear moduli levels and in ex vivo porcine heart tissue. For validation, an ultrasound array transducer was used in this study to visualize the propagation of the LSWs generated by the actuator. Spatiotemporal displacement maps were analyzed to estimate shear wave speeds and corresponding shear moduli, with TE results showing strong agreement with values obtained using conventional acoustic radiation force-based shear wave elasticity imaging (SWEI). The TE and SWEI measurements showed no statistically significant differences. Ex vivo tissue measurements performed in different orientations relative to myocardial fiber direction confirmed the system's sensitivity to tissue anisotropy. Additionally, the technique successfully distinguished between fresh and fixed heart tissue, detecting a noticeable increase in stiffness due to preservation. These findings support the feasibility of a catheter-integrated TE device as a functional extension of existing clinical workflows, offering quantitative assessment of myocardial elasticity during routine catheterization procedures.

The interaction of ultrasonic guided waves with defected structures gives rise to local defect resonance (LDR), which manifests in large displacements in the vicinity of the defect and affects the reflection and transmission spectra. This paper investigates the fundamental mechanism at the origin of the LDR phenomenon in isotropic elastic plates, using a hybrid computational method (Global-Local). The analyses show that the coupling of ultrasonic guided waves with the vibrational resonance modes of the sub-structure, geometrically defined by the defect, causes LDR, and boundary conditions affect it secondarily. The coupling mechanism is captured by the Global-Local method and is investigated in relation to the characteristics of the defect and the relationship to the host-structure. The coupling occurs at defect lengths that are odd multiples of the modes' quarter wavelengths. Comparisons with analytical, finite element and methods in literature for the computation of the natural and LDR frequencies are provided. The presence of LDR and its effect on broadband reflection and transmission ultrasonic spectra away from the defected region are also verified experimentally and can be used for remote defect characterization in NDE applications. These studies clarify the fundamental understanding of LDR and provide an effective approach to capture and predict LDR in ultrasonic guided wave propagation in plate-like structures.