Ultrasonics最新文献

Ultrasonic phased array testing is commonly employed for inspecting curved structures. Conventional plane wave imaging techniques, based on delay-and-sum in the time-domain, offer high image quality and inspection accuracy but suffer from low frame rates due to their high computational complexity. In this work, an efficient wavenumber-domain imaging method that combines non-stationary wavefield extrapolation and $f$-$k$ migration is proposed for curved structure inspection. Special emission focal laws are designed to generate a sequence of steered plane waves through the curved interface. The raw data is then extrapolated to the top boundary of the region of interest, followed by $f$-$k$ migration to reconstruct images with high time efficiency. Simulation and experimental evaluations demonstrate a time reduction by a factor of up to 32.24 compared to conventional time-domain plane wave image reconstruction with equivalent image quality, highlighting its potential for monitoring flaws in real-time.

Viscoelastic materials will absorb and dissipate energy under stress, resulting in energy loss and heat generation. The conventional non-destructive testing methods have certain limitations when it comes to detecting near-surface defects in viscoelastic materials. In this paper, a detection method of near-surface defects based on focused ultrasonic thermal effect is proposed. Firstly, the difference in thermal effects caused by defective and non-defective regions of the material under ultrasound is analyzed according to the stress response equation of viscoelastic materials, and the detection principle is elucidated. Secondly, the feasibility of this method is verified through finite element simulation with an example of plexiglass material Subsequently, the variations in the surface temperature distribution of defective specimens with varying diameters and depths are analyzed. Finally, experimental validation reveals that ultrasonic waves operating at 1.12 MHz successfully detect artificial defects with a diameter of 1 mm. With the increase of the equivalent diameter of the defect, the width of the low-temperature depression area in the surface temperature field exhibits a linear increase relationship. With the increase of the defect depth, the surface temperature difference between the corresponding position of the defective and the surrounding non-defective area gradually decreases. This method effectively overcomes the half-wavelength limitation and introduces a novel detection approach for near-surface defect identification in viscoelastic materials such as plexiglass.

The way of determining the acoustic power of longitudinal ultrasonic vibrations going into the load by measuring the amplitude of longitudinal displacements using an electrodynamic sensor installed near the surface of the waveguide rod is considered. Two possibilities of using the developed method of measurement are analyzed in detail. One of them is based on the registration of the value of longitudinal displacements at the constant position of the electrodynamic sensor in two different states of the ultrasound system: with the load disconnected and after the load is connected, i.e. it uses two measurements at different moments of time, when the states of the system differ from each other. Another way uses two measurements of the amplitude of longitudinal oscillations at two chosen points of the oscillatory system, made at the same time. Formulas have been obtained that make it possible to determine the power entering the load from the measured values and other known values of the system parameters. The role of errors, both in the readings of the sensor and in determining its location on the oscillating system, on the accuracy of calculating the value of the power of the ultrasonic oscillations that went into the load is analyzed.

The ultrasonic-assisted manufacturing process is a promising machining approach for composite materials as it exerts less force, making it ideal for the aerospace and automotive sectors. This work reports about the pre-crack initiation in carbon fiber reinforced (CF)/ poly-ether-ether-ketone (PEEK) composite under ultrasonic frequency at room temperature. An iron-based cutting tool matching the system’s resonance frequency (20 kHz) was used to perform the ultrasonic pre-cracking. In this novel work, the pre-cracking of CF-PEEK is considered as the initial step for a complete fiber layer separation, which holds the key for circularity options in high-performance aerospace composites. State-of-the-art high-speed camera and infrared thermography were combined to monitor the crack initiation and propagation. By online monitoring, the different stages involved in the pre-cracking process, its temperature evolution, and consequently the dissipated energy during pre-cracking under ultrasonic frequency were evaluated. The results showed that oscillation amplitude had a significant influence on the determined pre-crack depth and measured global temperature and energy compared to cutting force. The measured global temperature data indicates that pre-cracking occurred in the solid state with a temperature well below the glass-transition temperature of PEEK. However, the local temperature at the contact between the sample and sonotrode could have been much higher during ultrasonic cutting which needed further investigation. The computed global dissipated energy and temperature were only reliable at the pre-crack initiation site due to the limitation in the infrared thermography system.

In this paper, we present an experimental observation of the phenomenon known as zero-group velocity (ZGV) combined harmonic generation, which is induced by the mixing of counter-directional Lamb waves. We utilize internal resonant conditions to selectively choose the primary mode pair at specific frequencies for the purpose of combined harmonic generation. To detect the ZGV combined harmonic component, we propose a hybrid system that incorporates dual wedge-transducers for generation and a laser interferometric system for receiving. The appearance of the predicted S₁-ZGV combined harmonic at a specific mixing frequency is clearly observed in our experiments. Furthermore, we experimentally verify the controllability of the generated combined harmonics induced by the mixing of Lamb waves.

It is of practical importance to emit a pure wave mode, focus its energy along a given direction, and then steer the wave beam in guide-wave-based structural health monitoring (SHM) because it can quickly scan the overall structure. Such a goal is usually realized using a two-dimensional (2D) phased array, which requires many transducer elements and expensive electronics. This work proposed a radar transducer (RD-T) for unidirectionally emitting and steering the fundamental shear horizontal wave (SH₀ wave). The proposed RD-T consists of an annular metasubstrate and several rectangular thickness-shear (d₁₅) piezoelectric wafers. The metasubstrate is designed to provide the required phase gradient for unidirectionally emitting and sensing a pure SH₀ wave, so no extra time delay is required for driving the RD-T. The beam steering is obtained by activating the subunits one by one. The SH₀ wavefields generated by the subunit are described by a theoretical model and the effects of dimension parameters are analyzed. Finite element simulations and experiments are conducted to examine the performances of the RD-T. Both simulated and experimental results indicate that from 200 kHz to 270 kHz, the RD-T can unidirectionally emit an SH₀ wave with a high SNR (signal-to-noise ratio) and steer the wave beam along different directions. The performance of the RD-T on damage detection is then investigated by pulse-echo experiments. It can be found that the RD-T can successfully distinguish symmetric defects and locate defects with an acceptable error. Compared with the traditional 2D phased array, the RD-T can realize 360° scanning of the overall structure more efficiently, exhibiting great potential in the field of SHM.

Transcranial ultrasound imaging presents a significant challenge due to the intricate interplay between ultrasound waves and the heterogeneous human skull. The skull’s presence induces distortion, refraction, multiple scattering, and reflection of ultrasound signals, thereby complicating the acquisition of high-quality images. Extracting reflections from the entire waveform is crucial yet exceedingly challenging, as intracranial reflections are often obscured by strong amplitude direct waves and multiple scattering. In this paper, a multiple wave suppression method for ultrasound plane wave imaging is proposed to mitigate the impact of skull interference. Drawing upon prior research, we developed an enhanced high-resolution linear Radon transform using the maximum entropy principle and Bayesian method, facilitating wavefield separation. We detailed the process of wave field separation in the Radon domain through simulation of a model with a high velocity layer. When plane waves emitted at any steering angles, both multiple waves and first arrival waves manifested as distinct energy points. In the brain simulation, we contrasted the characteristic differences between skull reflection and brain-internal signal in Radon domain, and demonstrated that multiples suppression method reduces side and grating lobe levels by approximately 30 dB. Finally, we executed in vitro experiments using a monkey skull to separate weak intracranial reflection signals from strong skull reflections, enhancing the contrast-to-noise ratio by 85 % compared to conventional method using full waveform. This study deeply explores the effect of multiples on effective signal separation, addresses the complexity of wavefield separation, and verifies its efficacy through imaging, thereby significantly advancing ultrasound transcranial imaging techniques.

COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases.

Plane wave imaging (PWI) in medical ultrasound is becoming an important reconstruction method with high frame rates and new clinical applications. Recently, single PWI based on deep learning (DL) has been studied to overcome lowered frame rates of traditional PWI with multiple PW transmissions. However, due to the lack of appropriate ground truth images, DL-based PWI still remains challenging for performance improvements. To address this issue, in this paper, we propose a new unsupervised learning approach, i.e., deep coherence learning (DCL)-based DL beamformer (DL-DCL), for high-quality single PWI. In DL-DCL, the DL network is trained to predict highly correlated signals with a unique loss function from a set of PW data, and the trained DL model encourages high-quality PWI from low-quality single PW data. In addition, the DL-DCL framework based on complex baseband signals enables a universal beamformer. To assess the performance of DL-DCL, simulation, phantom and in vivo studies were conducted with public datasets, and it was compared with traditional beamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-based methods (i.e., supervised learning approach with 1-PW and generative adversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCL showed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatial resolution, and it outperformed all comparison methods in contrast resolution. These results demonstrated that the proposed unsupervised learning approach can address the inherent limitations of traditional PWIs based on DL, and it also showed great potential in clinical settings with minimal artifacts.

Early ultrasound screening for breast cancer reduces mortality significantly. The main evaluation criterion for breast ultrasound screening is the Breast Imaging-Reporting and Data System (BI-RADS), which categorizes breast lesions into categories 0–6 based on ultrasound grayscale images. Due to the limitations of ultrasound grayscale imaging, lesions with categories 4 and 5 necessitate additional biopsy for the confirmation of benign or malignant status. In this paper, the SAE-Net was proposed to combine the tissue microstructure information with the morphological information, thus improving the identification of high-grade breast lesions. The SAE-Net consists of a grayscale image branch and a spectral pattern branch. The grayscale image branch used the classical deep learning backbone model to learn the image morphological features from grayscale images, while the spectral pattern branch is designed to learn the microstructure features from ultrasound radio frequency (RF) signals. Our experimental results show that the best SAE-Net model has an area under the receiver operating characteristic curve (AUROC) of 12% higher and a Youden index of 19% higher than the single backbone model. These results demonstrate the effectiveness of our method, which potentially optimizes biopsy exemption and diagnostic efficiency.