Search Results (38)

Ultrasonic TOFD imaging, as an important non-destructive testing method, has a wide range of applications in pipeline girth weld inspection and testing. Due to the limited bandwidth of ultrasonic transducers, near-surface defects in the weld are masked and cannot be recognized, resulting in poor longitudinal resolution. Affected by the inherent diffraction effect of scattered acoustic waves, defect images have noticeable trailing, resulting in poor transverse resolution of TOFD imaging and making quantitative defect detection difficult. In this paper, based on the assumption of the sparseness of ultrasonic defect distribution, by constructing a convolutional model of the ultrasonic TOFD signal, the Orthogonal Matching Pursuit (OMP) sparse deconvolution algorithm is utilized to enhance the longitudinal resolution. Based on the synthetic aperture acoustic imaging model, in the wavenumber domain, backpropagation inference is implemented through phase transfer technology to eliminate the influence of diffraction effects and enhance transverse resolution. On this basis, the time-domain sparse deconvolution and frequency-domain synthetic aperture focusing methods mentioned above are combined to enhance the resolution of ultrasonic TOFD imaging. The simulation and experimental results indicate that this technique can outline the shape of defects with fine detail and improve image resolution by about 35%. Full article

Near-infrared light-triggered photocatalytic water treatment has attracted significant attention in recent years. In this novel research, rational sonochemical fabrication of Ag₂S/g-C₃N₄ nanocomposites with various compositions of Ag₂S (0–25) wt% was carried out to eliminate hazardous rhodamine B dye in a cationic organic pollutant model. g-C₃N₄ sheets were synthesized via controlled thermal annealing of microcrystalline urea. However, black Ag₂S nanoparticles were synthesized through a precipitation-assisted sonochemical route. The chemical interactions between various compositions of Ag₂S and g-C₃N₄ were carried out in an ultrasonic bath with a power of 300 W. XRD, PL, DRS, SEM, HRTEM, mapping, BET, and SAED analysis were used to estimate the crystalline, optical, nanostructure, and textural properties of the solid specimens. The coexistence of the diffraction peaks of g-C₃N₄ and Ag₂S implied the successful production of Ag₂S/g-C₃N₄ heterojunctions. The band gap energy of g-C₃N₄ was exceptionally reduced from 2.81 to 1.5 eV with the introduction of 25 wt% of Ag₂S nanoparticles, implying the strong absorbability of the nanocomposites to natural solar radiation. The PL signal intensity of Ag₂S/g-C₃N₄ was reduced by 40% compared with pristine g-C₃N₄, implying that Ag₂S enhanced the electron–hole transportation and separation. The rate of the photocatalytic degradation of rhodamine B molecules was gradually increased with the introduction of Ag₂S on the g-C₃N₄ surface and reached a maximum for nanocomposites containing 25 wt% Ag₂S. The radical trapping experiments demonstrated the principal importance of reactive oxygen species and hot holes in destroying rhodamine B under natural solar radiation. The charge transportation between Ag₂S and g-C₃N₄ semiconductors proceeded through the type I straddling scheme. The enriched photocatalytic activity of Ag₂S/g-C₃N₄ nanocomposites resulted from an exceptional reduction in band gap energy and controlling the electron–hole separation rate with the introduction of Ag₂S as an efficient photothermal photocatalyst. The novel as-synthesized nanocomposites are considered a promising photocatalyst for destroying various types of organic pollutants under low-cost sunlight radiation. Full article

Surface angled cracks on critical components in high-speed machinery can lead to fractures under stress and pressure, posing a significant threat to the operational safety of equipment. To detect surface angled cracks on critical components, this paper proposes a “Quantitative Detection Method for Surface Angled Cracks Based on Full-field Scanning Data”. By analyzing different ultrasonic signals in the full-field scanning data from laser ultrasonics, the width, angle, and length of surface angled cracks can be determined. This study investigates the propagation behavior of ultrasonic waves and their interaction with surface angled cracks through theoretical calculations. The crack width is solved by analyzing the distribution of Rayleigh waves in the full-field scanning data. This paper also discusses the differences in ultrasonic wave propagation between near-field and far-field detection and identifies the critical point between these regions. Different computational methods are employed to calculate the inclination angle and the crack endpoint at various scan positions. Four sets of experiments were conducted to validate the proposed method, with results showing that the errors in determining the width, angle, and length of the surface angled cracks were all within 5%. This confirms the feasibility of the method for detecting surface angled cracks. The quantitative detection of surface angled cracks on critical components using this method allows for a comprehensive assessment of the component’s condition, aiding in the prediction of service life and the mitigation of operational risks. This method shows promising application potential in areas such as aircraft engine blade inspection and gear inspection. Full article

Time-of-Flight Diffraction (TOFD) is a method of ultrasonic testing (UT) that is widely established as a non-destructive technique (NDT) mainly used for the inspection of welds. In contrast to other established UT techniques, TOFD is capable of identifying discontinuities regardless of their orientation. This paper proposes a redesign of the typical TOFD transducers, featuring an innovative curved sole aimed at enhancing their defect detection capabilities. This design is particularly beneficial for thick-walled samples, as it allows for deeper inspections without compromising the resolution near the surface area. During this research, an evaluation consisting in simulations of the ultrasonic beam distribution and experimental tests on a component with artificially manufactured defects at varying depths has been performed to validate the new design. The results demonstrate a 30 to 50% higher beam distribution area as well as an improvement in the signal-to-noise ratio (SNR) resulting in a 24% enhancement in the capability of defect detection compared to the traditional approach. Full article

High-density polyethylene (HDPE) pipes are widely used for urban natural gas transportation. Pipes are usually welded using the technique of thermal butt fusion, which is prone to manufacturing defects that are detrimental to safe operation. This paper proposes a spatiotemporal singular value decomposition preprocessing improved total focusing method (STSVD-ITFM) imaging algorithm combined with ultrasonic phased array technology for non-destructive testing. That is, the ultrasonic real-value signal data are first processed using STSVD filtering, enhancing the spatiotemporal singular values corresponding to the defective signal components. The TFM algorithm is then improved by establishing a composite modification factor based on the directivity function and the corrected energy attenuation factor by adding angle variable. Finally, the filtered signal data are utilized for imaging. Experiments are conducted by examining specimen blocks of HDPE materials with through-hole defects. The results show the following: the STSVD-ITFM algorithm proposed in this paper can better suppress static clutter in the near-field region, and the average signal-to-noise ratios are all higher than the TFM algorithm. Moreover, the STSVD-ITFM algorithm has the smallest average error among all defect depth quantification results. Full article

Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs is crucial. Introducing a smart cane tailored for the blind can greatly improve their daily lives. This paper introduces a significant technical innovation, presenting a smart cane equipped with dual ultrasonic sensors for obstacle detection, catering to the visually impaired. The primary focus is on developing a versatile device capable of operating in diverse conditions, ensuring efficient obstacle alerts. The strategic placement of ultrasonic sensors facilitates the emission and measurement of high-frequency sound waves, calculating obstacle distances and assessing potential threats to the user. Addressing various obstacle types, two ultrasonic sensors handle overhead and ground-level barriers, ensuring precise warnings. With a detection range spanning 2 to 400 cm, the device provides timely information for user reaction. Dual alert methods, including vibrations and audio signals, offer flexibility to users, controlled through intuitive switches. Additionally, a Bluetooth-connected mobile app enhances functionality, activating audio alerts if the cane is misplaced or too distant. Cost-effective implementation enhances accessibility, supporting a broader user base. This innovative smart cane not only represents a technical achievement but also significantly improves the quality of life for visually impaired individuals, emphasizing the social impact of technology. The research underscores the importance of technological research in addressing societal challenges and highlights the need for solutions that positively impact vulnerable communities, shaping future directions in research and technological development. Full article

The challenge for ultrasonic (US) power transfer systems, in implanted/wearable medical devices, is to determine when misalignment occurs (e.g., due to body motion) and apply directional correction accordingly. In this study, a number of machine learning algorithms were evaluated to classify US transducer misalignment, based on data signal transmissions between the transmitter and receiver. Over seven hundred US signals were acquired across a range of transducer misalignments. Signal envelopes and spectrograms were used to train and evaluate machine learning (ML) algorithms, classifying misalignment extent. The algorithms included an autoencoder, convolutional neural network (CNN) and neural network (NN). The best performing algorithm, was deployed onto a TinyML device for evaluation. Such systems exploit low power microcontrollers developed specifically around edge device applications, where algorithms were configured to run on low power, restricted memory systems. TensorFlow Lite and Edge Impulse, were used to deploy trained models onto the edge device, to classify signals according to transducer misalignment extent. TinyML deployment, demonstrated near real-time (<350 ms) signal classification achieving accuracies > 99%. This opens the possibility to apply such ML alignment algorithms to US arrays (capacitive micro-machined ultrasonic transducer (CMUT), piezoelectric micro-machined ultrasonic transducer (PMUT) devices) capable of beam-steering, significantly enhancing power delivery in implanted and body worn systems. Full article

This paper proposes a method for the detection and depth assessment of tiny defects in or near surfaces by combining laser ultrasonics with convolutional neural networks (CNNs). The innovation in this study lies in several key aspects. Firstly, a comprehensive analysis of changes in ultrasonic signal characteristics caused by variations in defect depth is conducted in both the time and frequency domains, based on discrete frequency spectra and original A—scan signals. Continuous wavelet transform (CWT) is employed to obtain wavelet time–frequency maps, demonstrating the consistent characteristics of this image with crack depth variations. A crucial innovation in this research involves the targeted design and optimization of the model based on the characteristics of ultrasonic signals and dataset size. This includes aspects such as data preparation, CNN architecture construction, and hyperparameter selection. The model is tested using a random validation set, which effectively demonstrates the CNN model’s validity and high precision. The proposed method enables the recognition and depth assessment of tiny defects on or near surfaces. Full article

In deep mining, “critical static stress + slight disturbance” is an important inducing form of coal mine rockburst disasters. In previous studies, the critical static stress has been shown to be consistent with the loading direction of a slight disturbance but cannot reflect all types of rockbursts. In addition, the research that uses microseismic (MS) signals to reflect the overall process and critical stages of coal failure and instability under weak-energy and low-frequency disturbance conditions is immature, and more information, such as the critical state, has not been fully revealed. The aims of this paper are to further elucidate the important role of weak-energy and low-frequency disturbances in the occurrence of rockburst disasters. First, briquette samples were prepared from the Tashan Coal Mine, which is severely affected by rockbursts, and their homogeneity was verified using ultrasonic longitudinal wave velocity. Second, the natural frequency of the coal sample specimens was measured using a testing system. Then, based on the self-developed static pressure loading system, dynamic and static combined loading test system and MS signal monitoring device, the MS signal characteristics during the process of coal body failure and instability were comprehensively analysed. Finally, a comparison was made between weak-energy and low-frequency disturbances and impact disturbances. The results are summarized as follows. (1) The longitudinal wave velocity test results reflect that the briquette samples prepared in the experiment have high homogeneity. The smaller the particle size is, the higher the density and moulding pressure, and the denser the sample. (2) The natural frequency of the briquette samples is between 30.79 Hz and 43.34 Hz, and most of them fluctuate at approximately 35 Hz. (3) During the static loading stage, the occurrence of more than three MS signals of larger magnitude in a continuous cluster is an important criterion for the critical failure of the samples. (4) The weak-energy and low-frequency disturbance actually leads to fatigue damage, and the briquette sample experiences three stages: the near-threshold stage, the high-speed expansion stage and the final fracture stage. The smaller the particle size of the coal sample, the denser the specimen, the stronger the amplitude and energy of the single effective MS signal formed during the destruction process, the longer the time duration of crack expansion from the near-threshold stage to the high-speed expansion stage, and the stronger the ability of the coal sample to resist weak-energy and low-frequency disturbances. This study may contribute to a more comprehensive understanding of the destabilization mechanism of coal bodies and MS signal characteristics under weak-energy and low-frequency disturbances and provide a reference for further research and discussion. Full article

Countersunk head riveted joints (CHRJs) are essential for the aerospace and marine industries. Due to the stress concentration, defects may be generated near the lower boundary of the countersunk head parts of CHRJs and require testing. In this paper, the near-surface defect in a CHRJ was detected based on high-frequency electromagnetic acoustic transducers (EMATs). The propagation of ultrasonic waves in the CHRJ with a defect was analyzed based on the theory of reflection and transmission. A finite element simulation was used to study the effect of the near-surface defect on the ultrasonic energy distribution in the CHRJ. The simulation results revealed that the second defect echo can be utilized for defect detection. The positive correlation between the reflection coefficient and the defect depth was obtained from the simulation results. To validate the relation, CHRJ samples with varying defect depths were tested using a 10-MHz EMAT. The experimental signals were denoised using wavelet-threshold denoising to improve the signal-to-noise ratio. The experimental results demonstrated a linearly positive correlation between the reflection coefficient and the defect depth. The results further showed that high-frequency EMATs can be employed for the detection of near-surface defects in CHRJs. Full article

To conduct the ultrasonic weld inspection of polyethylene pipes, it is necessary to use low-frequency transducers due to the high sound energy attenuation of polyethylene. However, one of the challenges in this process is that the blind zone of the ultrasonic transducer may cover a part of the workpiece being tested. This leads to a situation where if a defect appears near the surface of the workpiece, its signal will be buried by the blind zone signal. This hinders the early identification of defects, which is not favorable in such a scenario. To address this issue, we propose a new approach to detect and locate the near-surface defects. We begin by performing a synchro-squeezing transform on the original A-scan signal to obtain an accurate time-frequency distribution. While successful in detecting and localizing near-surface defects, the method alone fails to identify the specific type of defect directly: a limitation shared with other signal processing methods. Thus, an effective and lightweight defect identification model was established that combines depth-wise separable convolution and an attention mechanism. Finally, the performance of the proposed model was compared and visually analyzed with other models. This paper successfully achieves the detection, localization, and identification of near-surface defects through the synchro-squeezing transform and the defect identification model. The results show that our model can identify both general and near-surface defects with an accuracy of 99.50% while having a model size of only 1.14 MB. Full article

A typical ultrasonic sensor has a major lobe that extends beyond 45 degrees. Because the wide beam of the ultrasonic sensor’s main lobe, which is used for straightforward distance measurement, has a low angular resolution, conventional methods such as incidence angle and linear angle measurements cannot accurately determine the azimuthal angle. Determining whether one or more objects are present in a single beam is also challenging. In this study, the azimuthal angles of two or more objects placed beneath a single beam are determined by the Doppler frequency shift. An ultrasonic sensor is mounted on an automobile to transmit and receive an ultrasound when the car moves towards stationary objects. The sensor picks up the object’s reflected Doppler shift signal. The azimuth angle of the objects is determined by estimating the received Doppler shift signal using a standard signal processing method. Near-field motion detection systems and autonomous driving heavily rely on the ability to evaluate the azimuthal angle of objects in a vehicle’s surroundings using the Doppler Effect. These are examples of low-cost technology and active safety, which the experimental results support. Based on the results and error estimation, there is an average error of less than 3% between measured and computed values. Full article

Can ordinary Micro-Electro-Mechanical-Systems (MEMS) microphones be used for near-ultrasonic applications? Manufacturers often provide little information about the signal-to-noise ratio (SNR) in the ultrasound (US) range and, if they do, the data are often determined in a manufacturer-specific manner and are generally not comparable. Here, four different air-based microphones from three different manufacturers are compared with respect to their transfer functions and noise floor. The deconvolution of an exponential sweep and a traditional calculation of the SNR are used. The equipment and methods used are specified, which makes it easy to repeat or expand the investigation. The SNR of MEMS microphones in the near US range is mainly affected by resonance effects. These can be matched for applications with low-level signals and background noise such that the highest possible SNR can be achieved. Two MEMS microphones from Knowles performed best for the frequency range from 20 to 70 kHz; above 70 kHz, an Infineon model delivered the best performance. Full article

High-frame-rate imaging with a clutter filter can clearly visualize blood flow signals and provide more efficient discrimination with tissue signals. In vitro studies using clutter-less phantom and high-frequency ultrasound suggested a possibility of evaluating the red blood cell (RBC) aggregation by analyzing the frequency dependence of the backscatter coefficient (BSC). However, in in vivo applications, clutter filtering is required to visualize echoes from the RBC. This study initially evaluated the effect of the clutter filter for ultrasonic BSC analysis for in vitro and preliminary in vivo data to characterize hemorheology. Coherently compounded plane wave imaging at a frame rate of 2 kHz was carried out in high-frame-rate imaging. Two samples of RBCs suspended by saline and autologous plasma for in vitro data were circulated in two types of flow phantoms without or with clutter signals. The singular value decomposition was applied to suppress the clutter signal in the flow phantom. The BSC was calculated using the reference phantom method, and it was parametrized by spectral slope and mid-band fit (MBF) between 4–12 MHz. The velocity distribution was estimated by the block matching method, and the shear rate was estimated by the least squares approximation of the slope near the wall. Consequently, the spectral slope of the saline sample was always around four (Rayleigh scattering), independently of the shear rate, because the RBCs did not aggregate in the solution. Conversely, the spectral slope of the plasma sample was lower than four at low shear rates but approached four by increasing the shear rate, because the aggregations were presumably dissolved by the high shear rate. Moreover, the MBF of the plasma sample decreased from $-$36 to $-$49 dB in both flow phantoms with increasing shear rates, from approximately 10 to 100 s⁻¹. The variation in the spectral slope and MBF in the saline sample was comparable to the results of in vivo cases in healthy human jugular veins when the tissue and blood flow signals could be separated. Full article

In the past twenty years, the basic investigation of innovative Non-Linear Optical (NLO) crystals has received significant attention, which has built the crucial heritage for the use of NLO materials. Fundamental research is essential given the scarcity of materials for NLO compounds, especially in the deep ultraviolet (DUV) and middle- and far-infrared (MFIR) regions. In the present work, we synthesized high-quality MFIR SbI₃·3S₈ NLO crystals having a length in the range of 1–5 mm through rapid facile liquid phase ultrasonic reaction followed by the assistance of instantaneous natural evaporation phenomenon of the solvent at room temperature. X-ray diffraction (XRD) results ratify the hexagonal R3m structure of SbI₃·3S₈ crystal, and energy-dispersive X-ray spectroscopy (EDX) demonstrates that the elemental composition of SbI₃·3S₈ crystal is similar to that of its theoretical composition. The direct and indirect forbidden energy gaps of SbI₃·3S₈ were measured from the optical transmittance spectra and they were shown to be 2.893 eV and 1.986 eV, respectively. The green sparkling signal has been observed from the crystal during the second harmonic generation (SHG) experiment. Therefore, as inorganic adducts are often explored as NLO crystals, this work on the MFIR SbI₃·3S₈ NLO crystal can bring about additional investigations on this hot topic in the near future. Full article