Search Results (423)

During the machining processes, surfaces are often contaminated by cutting fluids, metallic debris, and residual films, which may compromise subsequent operations (e.g., coating, bonding, or precision assembly). In the present study, the effectiveness of several cleaning methods applied to machined metallic surfaces was experimentally evaluated. A set of commonly used industrial metals, including stainless steels, alloy steels, aluminum alloys, and brass, was machined under controlled conditions and subjected to various cleaning treatments, including solvent-based cleaning, ultrasonic washing, and aqueous detergent processes. Surface conditions were first assessed through optical microscopy, focusing on machining grooves as preferential sites for contaminant accumulation. Then, scanning electron microscopy (SEM) combined with energy dispersive X-ray spectroscopy (EDS) was employed to better identify residual contaminants. Optical observations highlighted the progressive removal of debris and lubricant residues, while SEM–EDS analyses revealed the presence of thin organic films and localized carbon-rich contaminants, even on apparently clean surfaces. Results show a consistent trend across all materials, with increasing cleaning effectiveness from solvent-based treatments to ultrasonic cleaning and specific aqueous detergent processes. Ultrasonic cleaning proved particularly effective in removing thin films and contaminants in complex geometries, whereas aqueous detergent treatment demonstrated superior performance in eliminating larger debris and achieving overall surface cleanliness. The findings, combining a broad experimental campaign across multiple materials, cleaning treatments, and characterization techniques, underline the importance of multi-scale characterization for a reliable assessment of cleaning efficiency and suggest that combined cleaning approaches may further enhance surface quality in demanding industrial applications. Full article

In this paper, an intelligent localization framework based on deep learning is proposed to address the limitations of insufficient accuracy and robustness in defect identification and localization during the ultrasonic guided-wave non-destructive testing (NDT) of receiver tubes in tower-type molten salt Concentrated Solar Power (CSP) stations. In the proposed method, a 1D convolutional neural network (1D-CNN) initially processes raw time-series-guided wave signals, achieving coarse identification and preliminary localization of defective segments. Then, Mel spectrograms are employed to exploit multi-dimensional features in the time–frequency domain and transform 1D signals into 2D representations, thereby enriching feature diversity. A regression-based 2D-CNN was designed to predict the start and end points of defect segments, enabling precise interval localization. Furthermore, the Tree Seed Algorithm (TSA) was integrated to jointly optimize key hyperparameters, enhancing training efficiency and prediction accuracy. Experimental validation on a dataset of ultrasonic guided-wave signals from molten salt receiver tubes demonstrates that the TSA-optimized Mel+2D-CNN model achieves superior performance, with a Mean Absolute Error (MAE) of 75.11 sampling points and a Coefficient of Determination (R²) of 0.90. At an Intersection over Union (IoU) threshold of 0.3, the model achieves a hit rate of 89.21%, exhibiting significantly higher localization accuracy and stability compared to the 1D-CNN baseline model. These findings indicate that the proposed method effectively enhances the accuracy and robustness of guided wave-based defect localization in slender structures. While promising, the model’s generalization capability remains dependent on the data distribution and operating conditions; future work will focus on validating its engineering applicability across diverse, multi-scenario industrial environments. Full article

Autonomous driving in underground mines faces significant challenges due to Global Navigation Satellite System (GNSS) denial and harsh environmental conditions. Mainstream multi-sensor fusion and Simultaneous Localization and Mapping (SLAM) schemes have achieved substantial progress in underground navigation, but their deployment in feature-sparse tunnels may still face challenges related to computational burden and perception robustness. This study explores an infrastructure-assisted navigation architecture that transforms the roadway into a structured luminous guidance channel by deploying programmable Light Emitting Diode (LED) strips along the tunnel roof. The proposed system simplifies complex three-dimensional pose estimation into a two-dimensional visual servoing task targeting optical signals. Central to this approach is a robust data fusion strategy that utilizes a topology matching algorithm to map noisy Ultra-Wide-band (UWB) coordinates onto a discrete LED index space, thereby providing a reliable global positioning reference. Furthermore, a hierarchical fault-tolerant controller based on a Finite State Machine (FSM) is designed to facilitate seamless degradation to a UWB-assisted ultrasonic wall-following mode in the event of visual degradation, supporting fault-tolerant operation under controlled laboratory conditions. Experimental results in a laboratory simulation environment demonstrate that the system achieves millimeter-level static initialization accuracy, a dynamic tracking Root Mean Square Error of approximately 4 cm, and a 100% autonomous recovery rate from visual failures in straight tunnels. These results demonstrate the feasibility of the proposed infrastructure-assisted route under controlled laboratory conditions and suggest its potential as an engineering reference for structured underground transport scenarios with acceptable infrastructure modification. Full article

The prediction of the compressive strength (CS) for sustainable concrete reinforced with graphene nanoplatelets (GNPs) is difficult as a result of nonlinear interactions between chemical composition, dispersion state, and curing conditions. To address this, an interpretable ensemble machine learning framework is developed to provide accurate predictions of CS. The major input parameters used are sand content, graphene diameters, graphene thicknesses, and percentages of GNP to sand (GNP%; w/w), water-to-cement ratio W/C, ultrasonication period UST time (s), curing age CA day(s), while the CS (in MPa) is the target output. The random forest (RF) and XGBoost (XGB) models are incorporated into two novel metaheuristic optimization techniques, the Drawer-based optimization algorithm (DOA) and the Giant Trevally Optimizer (GTO), to enhance hyperparameter tuning and generalization. For all models, DOA XGB hybrids are the most predictive, with testing R² values up to 0.98; RMSE of around 2.9 MPa; MAE is approximately 2.0 MPa, and well over 97% within ±20% prediction error boundaries. The explainable artificial intelligence methodologies like Shapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), partial dependence plots, and Individual Conditional Expectation plots reveal curing age and graphene thickness as the dominant parameters. High strengths above 70 MPa are always achieved from higher curing age, w/c ratio (from 0.3 to 0.4), and graphene dosage (from 0.5 to 2.5%). A Python GUI is developed for efficient and accurate strength predictions suitable for practical applications. The proposed approach provides a robust, interpretable, and efficient alternative to extensive testing for GNP-reinforced concrete. Full article

This study evaluated changes in upper trapezius muscle stiffness and choroidal blood flow velocity before and after ultrasonic therapy of the trapezius muscle. Participants included 27 healthy young adults in their 20 s (median age [Q1–Q3]: 21.0 [19.3–21.0]) without subjective shoulder pain. All participants received a single-session ultrasound intervention, and no control group was included. Intraocular pressure (IOP), systolic blood pressure (BP), diastolic BP, mean BP, heart rate (HR), ocular perfusion pressure (OPP), and salivary α-amylase (sAA) activity, a marker of autonomic nerve function, were assessed at baseline and after therapy. Stiffness of the upper trapezius muscle was evaluated using shear wave elastography, and choroidal hemodynamics were assessed by measuring the mean blur ratio (MBR), a relative index of macular blood flow velocity, using laser speckle flowgraphy. IOP, systolic BP, diastolic BP, mean BP, HR, OPP, sAA activity, and MBR reduced significantly after therapy. The shear elastic modulus of the trapezius muscle also decreased significantly. However, no significant correlations were observed among the parameters. Among healthy adults in their 20 s without shoulder pain, trapezius muscle ultrasound therapy may enhance parasympathetic activity, contributing to decreases in systemic and choroidal circulatory parameters. These findings indicate that ultrasound therapy for shoulder stiffness may influence local musculoskeletal characteristics, systemic and ocular circulation, and autonomic pathways. Full article

Carbon Fiber Reinforced Polymers (CFRP) are extensively utilized in high-performance engineering, yet localized structural discontinuities can severely compromise their integrity. This paper aims to achieve high-sensitivity characterization of such anomalies using a proposed acoustic shearography technique based on continuous acoustic excitation. A comprehensive finite element model (FEM) was developed to clarify the mechanical-energy coupling between the acoustic fields and localized surface strain field modulations. By exploiting ultrasonic energy coupling, the localized features of discontinuities were identified through full-field, non-contact optical measurement of localized phase distortions. Key parameters, including shearing amount, excitation frequency, driving voltage, and geometric characteristics of blind flat-bottom holes (BFBH), were systematically investigated. The results demonstrate a high correlation between FEM simulations and experimental observations quantitatively elucidating how defect diameter and hole depth modulate surface strain distributions. The proposed hybrid acoustic optical approach achieves near-instantaneous full field imaging within a millisecond timeframe typically under 200 ms. Additionally, the methodology leverages localized acoustic resonance to significantly boost the signal-to-noise ratio (SNR) resulting in highly quantified phase map contrast. Full article

Compressed air leakages represent a major source of energy waste and financial loss in industrial facilities. However, accurately detecting and quantifying these leaks remains challenging due to the wide variation in the accuracy, cost, usability, and practical applicability of available methods. This paper presents a structured review and evaluation of leakage localization and quantification methods for compressed air systems (CASs), categorized into hardware-, software-, and non-technical-based approaches. Based on expert interviews and a comprehensive literature review, a set of evaluation criteria was defined and applied within a multi-criteria decision analysis (MCDA) framework. The Analytic Hierarchy Process (AHP) was used to derive criteria weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the alternatives separately for localization and quantification tasks. To enhance practical relevance, five expert interviews were conducted with industrial stakeholders from diverse professional backgrounds, including maintenance engineers and energy managers. A questionnaire was also distributed to assess the methods. The results provide illustrative insights into the relative suitability of different methods. Within the scope of this exploratory study, from a practical industrial perspective, the compressor duty cycle method and non-intrusive load monitoring (NILM) appear to be promising approaches to leakage quantification, while ultrasonic detection is preferred for localization. Notably, discrepancies between questionnaire-based rankings and expert interview insights highlight the limitations of purely survey-driven evaluations. The proposed framework supports industrial decision-makers in selecting leakage detection and quantification methods by balancing technical performance, implementation effort, and operational constraints, thereby contributing to reduced energy losses and improved system efficiency. Full article

This study aims to develop and characterize novel dermatocosmetic formulations designed to hydrate the skin, improve its appearance, reduce wrinkles, and provide antioxidant, anti-ageing, antimicrobial, and anti-inflammatory benefits, along with potential protection against UVA and UVB radiation. The formulations contain the following ingredients: xanthan gum (0.5%), Calendula officinalis oil (5%), Argania spinosa oil (5%), Helianthus annuus oil (5%), liposomes containing a hydroalcoholic extract of pomace from local red or white grapes (2%), an olive oil-based emulsifier (6%), vitamin E (0.5%), cetearyl alcohol (3%), propylene glycol (8%), and purified water (up to 100%). The natural ingredients used in these formulations, i.e., the red or white grape pomace extract from the aforementioned Romanian varieties, the oils of Calendula officinalis, Argania spinosa, and Helianthus annuus, xanthan gum, and the olive oil-based emulsifier (Olliva), promote the concept of ‘green cosmetics’. The use of liposomes to deliver bioactive substances from hydroalcoholic extracts allows the gradual release of active ingredients into the skin. An alternative for incorporating grape pomace extract into a cream-type matrix involves the use of liposomes. Liposomes loaded with red or white grape pomace extract were prepared using the thin-film hydration technique, followed by ultrasonication and extrusion. The obtained formulations were characterized using bio-physico-chemical analysis procedures in terms of consistency, colour, homogeneity, aroma, pH, stretch, texture, stability, and antioxidant activity/free radical scavenging capacity, as well as in vitro polyphenol release behaviour. These newly developed dermatocosmetic formulations were the subject of a patent application in Romania. Full article

To address the challenge of detecting internal defects in medium-thick titanium alloy laser welds, a combined simulation and experimental study on ultrasonic testing was conducted. A finite element model employing a 5 MHz shear wave angle transducer for inspecting titanium alloy welds was established. An ultrasonic testing system was developed, incorporating a DPR300 pulser-receiver (JSR Ultrasonics, Pittsford, NY, USA) and an MSO5204 oscilloscope (RIGOL, Suzhou, China), and was calibrated using standard reference blocks. The inspection results for four prefabricated internal defects at various depths demonstrated that all defects were effectively detected, with the minimum detectable equivalent defect size reaching 1 mm. The measured signal-to-noise ratio (SNR) averaged 17.6 dB, validating the high sensitivity of the proposed system. The mean absolute error for defect localization was 0.438 mm, achieving a positioning accuracy better than 0.5 mm. This study indicates that the pro-posed method enables effective detection and accurate localization of internal defects in titanium alloy laser welds, providing critical technical support for laser welding quality assessment. Full article

Acoustic microactuation technology has emerged as an effective approach for fabrication of micro- and nanoscale objects, enabling precise processing and shaping control of microscale materials by efficiently transmitting ultrasonic vibration energy and focusing energy locally. In this work, the proposed platform is regarded as an acoustically driven micromachine, in which ultrasonic excitation acts as the primary microactuation mechanism. Micrometer-scale copper wires are widely used in microelectronics and precision manufacturing. However, their small dimensions and low rigidity make fixation and forming particularly challenging. To achieve controllable forming of fine copper wires, this study introduces an ultrasonic vibration energy-focusing principle and investigates an ultrasonic post-processing method tailored for such materials, with the aim of enhancing processing stability and forming accuracy. An ultrasonic processing experimental platform for copper wires was established, and multiple micro-tool designs—including glass fiber, 304 stainless steel wire with support, and elastic hard 304 stainless steel—were evaluated. Single-point and continuous processing experiments were conducted by varying micro-tool materials and support configurations, and the influence of feed speed on processing width and depth was systematically analyzed. The results indicate that a hard 304 stainless steel micro-tool supported by a hard plastic ring provides the best overall performance. Feed speed has a significant effect on processing depth, with a maximum average depth of approximately 0.95 μm achieved at a feed speed of 1 mm/min. These findings demonstrate the feasibility of ultrasonic processing for the effective forming of fine copper wires and confirm that appropriate micro-tool design and feed speed are critical for achieving stable and reliable processing results. The proposed system employs an ultrasonically actuated micro-tool to perform post-processing on micrometer-scale copper wires. The ultrasonic vibration serves as a microactuation mechanism that enhances local deformation and material response during micro-machining. Full article

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article

For the first time, hybrid nanocomposites based on poly(2,5-dichloro-3,6-bis(phenylamino)-p-benzoquinone) (PCPAB) and multi-walled carbon nanotubes (MWCNTs) were obtained and the influence of the preparation method on their structure and functional properties was demonstrated. The nanocomposites were obtained both by ultrasonic mixing of PCPAB and MWCNTs, and via in situ oxidative polymerization of CPAB in the presence of MWCNTs or MWCNTs with the addition of ZnO. The formation of hybrid nanocomposites occurs due to non-covalent interaction (π-stacking) between the graphene structures of the MWCNT surface and the phenyl rings of PCPAB. It was found that during the in situ oxidative polymerization of CPAB in the presence of MWCNTs, the growth of polymer chains occurred in close proximity to the filler surface, which led to the formation of a polymer coating. ZnO particles, localized on MWCNTs, on the one hand, prevent their aggregation, and on the other hand, create additional polymerization reaction centers due to the coordination of the Zn-O bond at the H and O atoms of the monomer. An increase in the concentration of reaction centers as a result led to a 2–2.5-fold reduction in the induction polymerization period. According to SEM data, in this case, a more ordered and denser polymer layer is formed due to intermolecular complexation between the main and side chains of the growing polymer with the participation of Zn²⁺ ions formed as a result of the transformation of ZnO to ZnCl₂ in the acidic reaction medium of polymerization. The results of the study of the frequency dependences of conductivity indicate a hopping mechanism of conductivity of nanocomposites. The electrical conductivity of nanocomposites depends on their production method and the MWCNT content and varies between 0.5 and 1.1 S∙cm⁻¹, which is 6–12 times higher than the conductivity of the original polymer. Thermogravimetric analysis revealed that the nanocomposites exhibit enhanced thermal stability compared to PCPAB. The best results were shown by nanocomposites with a higher content of MWCNTs, for which the residual mass at 450 °C was 51–53%. Full article

Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aircraft skins due to their advantages of high strength and lightweight properties. However, their laminate structure and energy-absorbing characteristics result in low-energy impact damage, such as delamination, that is often invisible but can lead to catastrophic failure. Consequently, early detection of delamination in CFRP laminates is necessary. Nonlinear ultrasonic guided waves exhibit high sensitivity to delamination, and second harmonics are widely employed. Compared to second harmonics, one-way mixing of ultrasonic guided waves can excite and receive signals simultaneously at the same location, thereby precisely localizing delamination. This capability has the potential for inspecting buried structures. However, existing literature has not yet fully addressed the generation mechanism of one-way mixing in CFRP laminates nor its interaction with delamination. Based on finite element simulation, this study investigates one-way mixing of A₀ modes and S₀ modes in CFRP laminates. Utilizing pulse-inversion techniques and two-dimensional fast Fourier transforms, the modes and propagation directions of difference-frequency components and sum-frequency components are determined. Furthermore, by utilizing the normalized acoustic nonlinearity parameter χ’ and adjusting the position of the mixing zone through different time delays, delamination in the CFRP laminate is successfully localized. Full article

Ultrasonic sensing is an effective tool for characterizing heterogeneous concrete structures, yet quantitative interpretation of ultrasonic attenuation remains challenging due to aggregate-induced multiple scattering and spatial non-uniformity. This study proposes a path-integrated ultrasonic attenuation modeling framework for concrete with random aggregates. A quasi-one-dimensional discretized wave equation is coupled with a modified version of the Waterman–Truell effective medium theory, in which multiple scattering effects are corrected by incorporating a Percus–Yevick structure factor and a geometric equivalence scheme for non-spherical aggregates. By discretizing the propagation path into locally homogeneous layers, cumulative attenuation is evaluated through explicit path integration, allowing spatial variations in aggregate volume fraction to be captured. Low-frequency ultrasonic transmission experiments (25 kHz) are conducted using serially assembled concrete specimens with controlled aggregate contents. The results reveal pronounced path-dependent attenuation behavior governed by local aggregate distribution. Compared with classical and effective Waterman–Truell models, the proposed approach significantly improves prediction accuracy, achieving a mean absolute percentage error of 7.29%. The framework provides a physically interpretable and experimentally validated method for ultrasonic sensing of heterogeneous concrete, with potential applications in non-destructive evaluation and structural health monitoring of high-end concrete-based engineering structures. Full article

As a critical barrier for power network safety, insulating materials are susceptible to internal microcracks, delamination, and other hidden defects that can trigger dielectric strength degradation and space charge accumulation, ultimately leading to insulation breakdown. Ultrasonic shear wave non-destructive testing enables defect identification without damaging the material. Therefore, this paper focuses on the identification and imaging of internal defects in insulating components using ultrasonic shear waves. First, a physical model for ultrasonic shear wave NDT is established. Based on the refraction and reflection characteristics of ultrasonic waves in materials with different acoustic impedances, a defect localization formula is derived. Through simulation verification, for the three defects set at different positions in the defect model, the positioning error is less than 0.5 mm. Subsequently, defects such as circular holes, triangular shapes, cracks, and bottom grooves were simulated. Analysis of the echo data revealed a correlation between the distance from the sensor to the defect and the echo amplitude. For groove defect imaging, the differential SAFT algorithm was employed, achieving a width error of 1 mm for imaging a 2 mm wide by 5 mm high groove, clearly presenting the defect morphology. Finally, an imaging software program for defect structure reconstruction was developed based on the simulation model presented in this article. We collected side and back view data through the constructed ultrasonic transverse wave non-destructive testing experimental platform, and visualized defects in insulation materials with grooves using this ultrasonic imaging program. This study achieved defect localization and imaging through simulation of various defect types combined with synthetic aperture focused imaging algorithms, providing a reference for visualization and industrial application of ultrasonic shear wave non-destructive testing technology. Full article