Ultrasonics最新文献

While mammography is the standard modality for detecting microcalcifications (MCs), their real-time detection with ultrasound imaging can be invaluable, particularly for guiding biopsies. Ultrasound twinkling artifact (TA) imaging allows the sensitive distinction of MCs from background breast tissue; however, it may also be confounded with blood flow in Doppler mode during in vivo scanning. In this paper, we propose a new MC imaging method that classifies TA and blood flow signals to enable in vivo detection of breast MCs. Based on the signal characteristics of TA and blood flow, two optimal features (i.e., mean frequency and spectrum bandwidth) are extracted and used to train a machine learning classifier. To train the classification model, tissue-mimicking and chicken breast phantom containing normal wire (285 μm in diameter), MC wire (300 μm in diameter) and micro-vessel tube (1 mm in diameter) were fabricated, and training and validation datasets were acquired under varying flow velocities and pulse repetition frequencies (PRFs). Among the four classifiers, i.e., k-nearest neighbors (KNN), support vector machine (SVM), naïve Bayes and quadratic discriminant, trained with the two optimal features, the SVM achieved the highest accuracy (95.25 %), whereas the remaining models also exhibited strong performance with accuracies exceeding 92 %. The trained SVM model was then validated on a chicken breast MC phantom and in vivo human breast data, and they showed good agreement with color Doppler imaging. The feasibility study demonstrated that the proposed classification approach may enable effective in vivo detection and improve diagnostic accuracy, especially in cases with complex flow patterns in breast lesions.

The morphology of deposition layer in direct energy deposition-Arc (DED-Arc) critically affects the forming accuracy and performance of the manufactured components. To address morphology control challenges, this study investigates the role of ultrasonic amplitude in geometric characteristics and wetting behavior of mild steel deposition layers. An ultrasonic-assisted DED-Arc platform was established, integrating numerical models for ultrasonic field propagation and molten pool fluid dynamics to analyze temperature gradients under optimal ultrasonic excitation source. Simulation results revealed that positioning the ultrasonic excitation source at the substrate's geometric center achieved uniform vibration distribution. Ultrasonic vibration reduced the molten pool's maximum temperature gradient by 28.2% (horizontal) and 24.9% (vertical), enhancing thermal uniformity. Experimental findings demonstrated that increasing ultrasonic amplitude (0-16 μm) decreased deposition layer height (3.09-2.56 mm), depth (1.98-1.60 mm), contact angle (57°-44°) and dilution rate (38.1%-33.5%), while increasing width (8.21-9.04 mm). The surface roughness (Ra, Rz) decreased by 20.3% and 39.3% respectively. High-speed imaging of glycerol droplet spreading revealed that ultrasonic vibration reduced contact angles from 58° to 46° and increased spreading area by 38.2% within 2 s, demonstrating enhanced wettability. A critical threshold of 16 μm amplitude was identified, beyond which molten pool instability degraded morphology. Ultrasonic vibration enhanced wetting by generating a viscous momentum transfer layer at the melt-substrate interface, driving outward expansion of the triple-phase contact line. These results provide quantitative guidelines for optimizing DED-Arc processes in automotive and aerospace applications requiring precise morphology control.

Structural health monitoring often involves temperature measurement. However, traditional sensors cannot measure subsurface temperature non-invasively, making them unsuitable for monitoring temperature-driven damage mechanisms such as high-cycle thermal fatigue. This limitation arises, in part, due to effective thermal low-pass filtering caused by material properties. A previous feasibility study demonstrated that subsurface temperature can be inferred non-invasively in mild steel subjected to uniform heating. This was achieved using the ultrasonic-based inverse thermal modelling (ITM) method, which assumes the temperature of a component can be described by a 1D system. This study investigated the behaviour of ITM under non-uniform heating applied to the 'inaccessible' surface of a stainless steel sample through experiments and simulations. The experimental results show that ITM over-predicts temperature by as much as 120% when the heated region is small compared with the 10mm ultrasonic beam size. In simulation, the overestimation was reduced as the size of the heating source increased, effectively making the temperature distribution more uniform across the volume through which the ultrasonic wave travels. Despite the overestimation under non-uniform heating, ITM overcomes the thermal low-pass filtering, allowing the detection of thermal transients compared with a thermocouple mounted on the 'accessible' surface of a component.

This study investigated the feasibility of guided ultrasonic wave monitoring of bone attachment to uncemented orthopaedic implants during the rehabilitation process (osseointegration), which is crucial for implant stability and long-term survival. Experiments were conducted using a simplified three-layer synthetic bone model of an intraosseous transcutaneous amputation prosthesis (ITAP) implant, used for femoral amputee patients, where epoxy curing simulated the bone ingrowth process associated with increasing bone-implant interface layer stiffness, representing the early stages of osseointegration. Longitudinal guided wave signals were excited and recorded at the distal end of the percutaneous part of the stainless-steel implant. Finite element analysis (FEA) was validated from the experiments and employed to investigate the sensitivity and wave mode selection. FEA simulations showed frequency shifts and group velocity changes of the guided wave modes with increased osseointegration, matching theoretical predictions. Evaluation of the reflected wave pulse in the time domain for both experimental monitoring and FEA simulations showed a significant increase in arrival time (10%) and amplitude drop (>50%). The results showed that the longitudinal guided waves are sensitive to stiffness changes during the bone healing process and provide insights for the development of in-vivo osseointegration monitoring during patient rehabilitation.

Experimental observation of the piezoelectric signals generated in cancellous bone by an ultrasound wave was performed using "piezoelectric cells (PE-cells)". The PE-cell, in which a cancellous bone specimen is used as a piezoelectric element, can correspond to an ultrasound receiver. In this study, two cancellous bone specimens in which the pore spaces were saturated with air and two waters with normal and low conductivities were used. The piezoelectric signals generated in the air- and water-saturated cancellous bone specimens by an ultrasound wave and the ultrasound signals propagated through the specimens were observed. The amplitudes of the piezoelectric signals in the water-saturated cancellous bone specimens were approximately four times of the amplitude in the air-saturated specimen. Both fast and slow waves, which can propagate mainly in the trabecular elements and the pore fluid, respectively, could be observed for the ultrasound signals in the water-saturated cancellous bone specimens, but only the fast wave could be observed for the signal in the air-saturated specimen. From the observed results, it was suggested that the piezoelectric signal generated in cancellous bone by an ultrasound wave could be largely associated with the motion of the pore fluid.

Piezoelectric micromachined ultrasonic transducer (PMUT), owing to the miniaturization, low power consumption, and ease of driving, have become a viable alternative to traditional piezoelectric ceramic transducers in air-coupled ultrasonic ranging applications. However, PMUT suffer from significantly degraded transmission performance due to residual stresses inherent in microelectromechanical systems (MEMS) fabrication processes, which substantially limits their detection range and accuracy in airborne applications. To address this issue, this work presents a novel cantilever-based PMUT design, which incorporates micro-slits along the diagonal of the diaphragm to form four triangular cantilever beams. Additionally, inspired by springs, a flexible spring-folded beam structure is designed at the tail end of the cantilever beams to achieve cooperative vibration of the cantilevers through low stiffness mechanical coupling. This design significantly reduces the diaphragm stiffness, fully releases the residual stress, and enhances the mechanical response of the PMUT. Experimental results confirm that, under a low drive voltage of 1 V_PP (-5 V offset), the novel PMUT achieves a high resonant displacement of 16,752 nm at its resonant frequency of 73.67 kHz, representing an increase of 10,951 nm compared to the 4,823 nm displacement of the conventional PMUT. At a 10 cm air distance, the device generates a high sound pressure of 4.8 Pa, equivalent to 107.6 dB (Ref. 2 × 10^-5 Pa), which is approximately 6.86 dB higher than conventional PMUT. The new PMUT exhibits a receiving sensitivity of 0.85 mV/Pa, which is an improvement of 0.63 mV/Pa over conventional PMUT. This design significantly enhances the transmission performance of PMUT, showing great potential in high-precision air ranging applications.

Transcranial ultrasound (tUS) is a non-invasive neuromodulation technique with applications in brain disorders. However, ultrasound attenuation induced by the skull significantly affects focal energy transmission. The loss of ultrasound intensity can be quantified by insertion loss (IL). Accurate IL prediction is crucial for optimizing ultrasound delivery. Conventional grid-based numerical methods for IL prediction are computationally expensive and highly sensitive to parameter variations. To address these challenges, we hypothesized that skull structural features are inherently correlated with IL and can be effectively captured through deep learning method. In this study, we conducted transmission experiments on 20 human skull specimens to measure IL at three frequencies of 220 kHz, 650 kHz, and 1000 kHz. We proposed a modified dual-path Inception-based neural network (mDPI-Net) for IL prediction based on skull computed tomography (CT) scan. Comparison results showed that mDPI-Net outperformed homogeneous pseudo-spectral methods (Peak Pressure Error: 26.6% vs. 34.3%, IL Deviation: 2.47 dB vs. 4.64 dB), and is comparable to the inhomogeneous simulations (Peak Pressure Error: 26.6% vs. 21.0%, IL Deviation: 2.47 dB vs. 1.69 dB), while achieving higher computational efficiency, increasing from 15 min/sample to 0.5 s/sample. The proposed approach demonstrated that the skull CT scan could inherently encode structural information relevant to IL. Under a well-fixed experimental setup, deep learning has the potential to enable real-time or rapid pre-operative IL predictions, and achieve more precise dose control in tUS applications such as neuromodulation, transcranial drug delivery, and non-invasive brain stimulation.

Despite the effectiveness of methods based on local defect resonance (LDR) for the nondestructive evaluation of planar defects, the physical mechanism underlying the generation of LDR remains an ongoing topic of research interest. Existing methods for interpreting the generation of LDR are based on the vibration theory and simplified boundary conditions, but they demonstrate effectiveness for LDR frequency prediction only in defects within specific parameter ranges and lack universal applicability for both near surface and internal defects. A two-step approach is proposed in this investigation to understand the generation of LDR from the perspective of wave reflection and standing wave formation. In this approach, the interaction of guided waves with defect boundaries are analyzed using the normal mode expansion method, and thereby the phase shift of reflected wave modes is obtained. On this basis, the formation of standing waves is analyzed, and a quantitative relation between the defect parameters and LDR frequency can be obtained explicitly. The shape effect on the LDR frequency is then investigated via a Rayleigh method. The proposed approach provides an insight into the generation of LDR for both near surface and internal defects, and enables the quantitative evaluation of defects with circular and elliptical shapes using the LDR frequencies.

Accurate determination of elastic constants is crucial for reliable ultrasonic defect detection in carbon fiber reinforced plastic (CFRP). However, non-destructive in-situ characterization of these constants, particularly via full-waveform inversion techniques, is hindered by the computational cost of wavefield simulations. Based on physics-informed neural networks (PINNs), a novel longitudinal and shear wavefield net (LSWNet) method is proposed for the forward wavefield prediction and inversion of ultrasonic waves in a unidirectional CFRP. The longitudinal and shear wave component fields at two moments, ultrasonic measurement data, and the 2D elastic wave equations of isotropic and anisotropic planes for unidirectional CFRP are embedded as physical constraint conditions to predict wavefields and elastic constants. For the inversion of elastic constants, ultrasonic data recorded by a linear phased array on the CFRP surface serve as input, while the LSWNet outputs C₆₆, C₁₃ and C₄₄. To accelerate convergence in large-scale models, weights and biases learned from training on small-scale structures are transferred. The proposed method has been verified through both finite element simulation and experiments. The mean squared errors between the predicted wavefields by PINNs and those obtained from finite element simulation do not exceed 3.2 × 10^-3, and the obtained elastic constants are close to the actual values. Furthermore, the elastic constants obtained via LSWNet are successfully applied to total focusing method, thereby enabling high-resolution detection of delamination damage. Consequently, the proposed method is capable of resolving forward and inverse issues associated with unidirectional CFRP ultrasonic wavefields, as well as in-situ characterization of elastic constants and damage imaging.