Search Results (180)

Regenerative hardfacing of steel substrates is an important technology for restoring the surface layer of components operating under wear conditions, supporting the goals of the circular economy (CE) by extending the service life of components, reducing material and energy consumption throughout their life cycle, and shortening downtime during machine repairs. The article provides a synthetic analysis of the literature on the production of functional layers exclusively on steels and systematizes process → structure → properties (PSP) relationships in the context of technological quality and the prediction of the functional properties of welds. The review covers methods used and developed in steel hardfacing (including arc processes and variants with increased energy concentration), analyzed on the basis of measurable process indicators: energy parameters (arc energy/heat input/volume energy), dilution, bead geometry, heat-affected zone characteristics, and the risk of welding defects. It has been shown that these factors determine the structural effects in the weld and the area at the fusion boundary (including phase composition and morphology, hardness gradient, and susceptibility to cracking), which translates into functional properties (hardness, wear resistance, adhesion, and fatigue life) and durability after regeneration. The main result of the work is the development of a PSP table dedicated to hardfacing on steel substrates, mapping the key “levers” of the process to structural consequences and trends in functional properties. This facilitates the identification of optimization directions (minimization of energy input and dilution while ensuring fusion continuity), which translates into longer durability after regeneration and a lower risk of defects—key, measurable effects of CE. Research gaps have also been identified regarding the comparability of results (standardization of energy metrics) and the need to determine and verify “technology windows” within the WPS/WPQR (welding procedure specification/welding procedure qualification record) for layers deposited on steels. Full article

Ensuring friction stir welding (FSW) joint quality typically relies on ultrasonic testing (UT) and radiographic testing (RT), but achieving complete coverage is challenging, and echo-based defect discrimination becomes difficult in dissimilar joints. Laser ultrasonics is a promising non-contact technique that remotely assesses weld quality and provides high spatial resolution at the generation and detection points. This study establishes a laser-ultrasonic method for defect detection in dissimilar Cu–Al FSW joints. Slit-like artificial defects (0.1–2.5 mm deep in 5 mm thick plates) were introduced at the Al-side interface of specimens fabricated with an Al-offset tool. Experiments and numerical simulations were used to evaluate wave modes and irradiation configurations, focusing on intensity-attenuation ratios of specific wave types, including longitudinal and Rayleigh waves. On the non-slit surface, attenuation of reflected longitudinal waves enabled detection of defects ≥0.5 mm deep. On the slit surface, Rayleigh-wave attenuation allowed identification of defects as shallow as 0.1 mm, although slit-side irradiation may be less practical during joining. These results demonstrate that defect identification in dissimilar materials can be achieved by evaluating wave-intensity attenuation rather than relying solely on the presence of reflected echoes, suggesting potential for implementing laser ultrasonics in in-process monitoring of FSW joints. Full article

Cracks serve as a critical indicator of tunnel structural degradation. Manual inspections are difficult to meet engineering requirements due to their time-consuming and labor-intensive nature, high subjectivity, and significant error rates, while traditional image processing methods exhibit poor performance under complex backgrounds and irregular crack morphologies. To address these limitations, this study developed a high-quality dataset of tunnel crack images and proposed an improved lightweight semantic segmentation network, LiteSqueezeSeg, to enable precise crack identification and quantification. The model was systematically trained and optimized using a dataset comprising 10,000 high-resolution images. Experimental results demonstrate that the proposed model achieves an overall accuracy of 95.15% in crack detection. Validation on real-world tunnel surface images indicates that the method effectively suppresses background noise interference and enables high-precision quantification of crack length, average width, and maximum width, with all relative errors maintained within 5%. Furthermore, an integrated intelligent detection system was developed based on the MATLAB (R2023b) platform, facilitating automated crack feature extraction and standardized defect grading. This system supports routine tunnel maintenance and safety assessment, substantially enhancing both inspection efficiency and evaluation accuracy. Through synergistic innovations in lightweight network architecture, accurate quantitative analysis, and standardized assessment protocols, this research establishes a comprehensive technical framework for tunnel crack detection and structural health evaluation, offering an efficient and reliable intelligent solution for tunnel condition monitoring. Full article

Silicon nanowires (SiNWs) fabricated by Ag-assisted metal-assisted chemical etching (MACE) exhibit excellent light-trapping performance, yet their fragile high-aspect-ratio morphology severely limits reliable metallization in photovoltaic devices. Conventional electrode deposition methods often fail on dense SiNW arrays due to poor mechanical stability of the nanowire tips, leading to delamination, inhomogeneous coverage, and high contact resistance. In this work, we introduce a maskless laser-based truncation technique that selectively shortens MACE-derived SiNWs to controlled residual heights of 300–500 nm exclusively within the regions intended for electrode formation, while preserving the full nanowire morphology in active areas. A detailed parametric study of laser power, scanning speed, and pulse repetition frequency allowed the identification of an optimal processing window enabling controlled tip melting without damaging the nanowire roots or the crystalline silicon substrate. High-resolution SEM imaging confirms uniform planarization, well-preserved structural integrity, and the absence of subsurface defects in the laser-processed tracks. Optical reflectance measurements further demonstrate that introducing 2% and 5% truncated surface fractions—corresponding to the minimum and maximum metallized front-grid coverage in industrial Si solar cells—results in only a minimal reflectance increase, preserving the advantageous the light-trapping behavior of the SiNW texture. The proposed laser truncation approach provides a clean, scalable, and industrially compatible route toward creating electrode-ready surfaces on nanostructured silicon, enabling reliable metallization while maintaining optical performance. This method offers strong potential for integration into silicon photovoltaics, photodetectors, and nanoscale electronic and sensing devices. Full article

The development of advanced semiconductor-based catalysts for the rapid degradation of emerging pharmaceutical pollutants in water remains a critical challenge in environmental science. In this study, we present the synthesis, characterization, and catalytic performance of zinc oxide (ZnO) microtetrapods decorated with plasmonic Ag nanoparticles. These microtetrapods have been designed to enhance piezo-, photo-, and piezo-photocatalytic degradation of metronidazole (MNZ), a persistent antibiotic contaminant. ZnO microtetrapods were synthesized by high-temperature pyrolysis and using atmospheric-pressure microwave nitrogen plasma, followed by photochemical deposition of Ag nanoparticles at various precursor concentrations (0–1 mmol AgNO₃). The structural integrity of the samples was confirmed through X-ray diffraction (XRD) analysis, while the morphology was examined using scanning electron microscopy with energy-dispersive X-ray analysis (SEM-EDX). Additionally, spectroscopic analysis, including Raman, electron paramagnetic resonance (EPR), and photoluminescence (PL) spectroscopy, was conducted to verify the successful formation of heterostructures with adjustable surface loading of Ag. It has been shown that ZnO microtetrapods decorated with plasmonic Ag nanoparticles exhibit Raman-active properties. A systematic evaluation under photocatalytic, piezocatalytic, and combined piezo-photocatalytic conditions revealed a pronounced volcano-type dependence of catalytic activity on Ag content, with the 0.75 mmol composition exhibiting optimal performance. In the presence of both light irradiation and ultrasonication, the optimized Ag/ZnO composite exhibited 93% degradation of MNZ within a span of 5 min, accompanied by an apparent rate constant of 0.56 min⁻¹. This value stands as a significant improvement, surpassing the degradation rate of pristine ZnO by over 24-fold. The collective identification of defect modulation, plasmon-induced charge separation, and piezoelectric polarization as the predominant mechanisms driving enhanced reactive oxygen species (ROS) generation is a significant advancement in the field. These findings underscore the synergistic interplay between plasmonic and piezoelectric effects in oxide-based heterostructures and present a promising strategy for the efficient removal of recalcitrant water pollutants using multi-field activated catalysis. Full article

With the exponential growth of road transportation infrastructure, the need for pavement maintenance has increased significantly. Surface cracking represents a critical evaluation metric in roadway inspection. Conventional manual inspection methods impose substantial demands on personnel resources, time investment, and operational safety while being susceptible to subjective assessment biases. Leveraging advancements in computer vision technology, researchers have progressively investigated automated solutions for infrastructure defect identification. This study presents an enhanced deep learning framework for pavement crack detection within computer science applications, featuring three principal innovations: implementation of the SIoU loss function for improved boundary regression, adoption of the Mish activation function to enhance feature representation, and integration of the EfficientFormerV2 attention mechanism for optimized computational efficiency. Experimental validation confirms the technical feasibility of our approach, demonstrating measurable improvements in processing efficiency and computational speed compared to baseline methods. Full article

Underground gas storage (UGS) is critical to national reserves and seasonal peak-shaving, and its safe operation is integral to energy security. In UGS surface process pipelines, heterogeneous bimetal composite pipes—carbon-steel substrates lined with stainless steel—are widely used but susceptible under coupled thermal–pressure–flow loading to geometry-induced instabilities (local buckling, adhesion, and collapse), which can restrict flow, concentrate stress, and precipitate rupture and unplanned shutdowns. Conventional ultrasonic testing and magnetic flux leakage have limited sensitivity to such instabilities, while standard eddy-current testing is impeded by the ferromagnetic substrate’s high permeability and electromagnetic shielding. This study introduces magnetically saturated pulsed eddy-current testing (MS-PECT). A quasi-static bias field drives the substrate toward magnetic saturation, reducing differential permeability and increasing effective penetration; combined with pulsed excitation and differential reception, the approach improves defect responsiveness and the signal-to-noise ratio. A prototype was developed and evaluated through mechanistic modeling, numerical simulation, laboratory pipe trials, and in-service demonstrations. Field deployment on composite pipelines at the Hutubi UGS (Xinjiang, China) enabled rapid identification and spatial localization of liner collapse under non-shutdown or minimally invasive conditions. MS-PECT provides a practical tool for composite-pipeline integrity management, reducing the risk of unplanned outages, enhancing peak-shaving reliability, and supporting resilient UGS operations. Full article

After rough planing, defects such as wormholes and small patches of green bark residue and decay are often overlooked and misclassified. Strip-like defects, including splinters and chipped edges, are easily confused with the natural bamboo grain, and a single elongated defect is frequently fragmented into multiple detection boxes. This study proposes a modified TASNet-YOLO model, an improved detector built on YOLO11n. Unlike prior YOLO-based bamboo defect detectors, TASNet-YOLO is a mechanism-guided redesign that jointly targets two persistent failure modes—limited visibility of small, low-contrast defects and fragmentation of elongated defects—while remaining feasible for real-time production settings. In the backbone, a newly designed TriMAD_Conv module is introduced as the core unit, enhancing the detection of wormholes as well as small-area defects such as green bark residue and decay. The additive-gated C3k2_AddCGLU is further integrated at selected C3k2 stages. The combination of additive interaction and CGLU improves channel selection and detail retention, highlighting differences between splinters and chipped edges and bamboo grain strips, thereby reducing false positives and improving precision. In the neck, the neck replaces nearest-neighbor upsampling and CBS with SNI-GSNeck to improve cross-scale alignment and fusion. Under an acceptable real-time budget, predictions for splinters and chipped edges become more contiguous and better aligned to edges, while wormholes predictions are more circular and less noisy. Experiments on our in-house dataset (8445 bamboo-strip defect images) show that, compared with YOLO11n, the proposed model improves detection accuracy by 5.1%, achieves 106.4 FPS, and reduces computational costs by 0.4 GFLOPs per forward pass. These properties meet the throughput demand of 2 m/s conveyor lines, and the compact model size and compute footprint make edge deployment straightforward for fast online screening and preliminary quality grading in industrial production. Full article

Ensuring the structural integrity of mechanical components is a key challenge in industries such as automotive, aerospace, and energy. Conventional techniques for defect identification, including non-destructive testing (NDT) and the Finite Element Method (FEM), offer reliable solutions—yet FEM often requires intensive modeling work and high computational cost. To streamline the detection process, this study proposes a method based on orthogonal moment transforms applied to digital images. This fast and automated technique is particularly suited for integration into industrial vision systems. The approach consists in encoding the visual features of a component using continuous orthogonal moments (e.g., Zernike, Chebyshev, or Fourier), and analyzing the extracted descriptors to identify irregularities associated with surface cracks or structural flaws. Full article

Infrared thermography non-destructive testing technology has been widely used in the defect detection of composite structures due to its advantages, including non-contact operation, rapidity, low cost, and high precision. In this study, a laser-line scanning system combined with an infrared thermography was developed, along with a corresponding dynamic sequence image reconstruction method, enabling rapid localization of surface damages. Then, high-precision quantitative characterization of defect morphology in reconstructed images was achieved by integrating an edge gradient detection algorithm. The reconstruction method was validated through finite element simulations and experimental studies. The results demonstrated that the laser-line scanning thermography effectively enables both rapid localization of surface damages and precise quantitative characterization of their morphology. Experimental measurements of ceramic materials indicate that the relative error in detecting crack width is about 6% when the crack is perpendicular to the scanning direction, and the relative error gradually increases when the angle between the crack and the scanning direction decreases. Additionally, an alumina ceramic plate with micrometer-width cracks is inspected by the continuous laser-line scanning thermography. The morphology detection results are completely consistent with the actual morphology. However, limited by the spatial resolution of the thermal imager in the experiment, the quantitative identification of the crack width cannot be carried out. Finally, the proposed method is also effective for detecting surface damage of wrinkles in ceramic matrix composites. It can localize damage and quantify its geometric features with an average relative error of less than 3%, providing a new approach for health monitoring of large-scale ceramic matrix composite structures. Full article

Surface defects in hot-rolled steel strip alter the material’s properties and degrade its overall quality. Especially in real production environments, due to time sensitivity, lightweight Convolutional Neural Network models are suitable for inspecting these defects. However, in real-time applications, the acquired images are subjected to various degradations, including noise, motion blur, and non-uniform illumination. The performance of lightweight CNN models on degraded images is crucial, as improved performance on such images reduces the reliance on preprocessing techniques for image enhancement. Thus, this study focuses on analyzing pre-trained lightweight CNN models for surface defect classification in hot-rolled steel strips under degradation conditions. Six state-of-the-art lightweight CNN architectures—MobileNet-V1, MobileNet-V2, MobileNet-V3, NasNetMobile, ShuffleNet V2 and EfficientNet-B0—are evaluated. Performance is assessed using standard classification metrics. The results indicate that MobileNet-V1 is the most effective model among those used in this study. Additionally, a new performance metric is proposed in this study. Using this metric, the misclassification distribution is evaluated for concentration versus homogeneity, thereby facilitating the identification of areas for model improvement. The proposed metric demonstrates that the MobileNet-V1 exhibits good performance under both low and high degradation conditions in terms of misclassification robustness. Full article

Mineral identification technology is a key technology in the construction of intelligent mines. In ore classification and detection, mining scenarios present challenges, such as diverse ore types, significant scale variations, and complex surface textures. Traditional detection models often suffer from insufficient multi-scale feature representation and weak dynamic adaptability, leading to the missed detection of small targets and misclassification of similar minerals. To address these issues, this paper proposes an efficient multi-scale ore classification and detection model, YOLO-EDH. To begin, standard convolution is replaced with deformable convolution, which efficiently captures irregular defect patterns, significantly boosting the model’s robustness and generalization ability. The C3k2 module is then combined with a modified dynamic convolution module, which avoids unnecessary computational overhead while enhancing the flexibility and feature representation. Additionally, a content-guided attention fusion (HGAF) module is introduced before the detection phase, ensuring that the model assigns the correct importance to various feature maps, thereby highlighting the most relevant object details. Experimental results indicate that YOLO-EDH surpasses YOLOv11, improving the precision, recall, and mAP50 by 0.9%, 1.7%, and 1.6%, respectively. In conclusion, YOLO-EDH offers an efficient solution for ore detection in practical applications, with considerable potential for industries like intelligent mine resource sorting and safety production monitoring, showing notable commercial value. Full article

Graphene/h-BN/Si heterostructures show considerable potential for future use in infrared detection and photovoltaic technologies due to their adjustable electrical behavior and well-matched interfacial structure. The near-lattice match between graphene and hexagonal boron nitride (h-BN) enables the deposition of low-defect-density graphene on h-BN surfaces. This study presents a thorough exploration of the low-frequency electrical noise behavior of graphene/h-BN/Si heterojunctions under both forward and reverse bias conditions at room temperature. Graphene nanolayers were directly grown on h-BN films using microwave plasma-enhanced CVD. The h-BN layers were formed by reactive high-power impulse magnetron sputtering (HIPIMS). Four h-BN thicknesses were examined: 1 nm, 3 nm, 5 nm, and 15 nm. A reference graphene/Si junction (without h-BN) prepared under identical synthesis conditions was also studied for comparison. Low-frequency noise analysis enabled the identification of dominant charge transport mechanisms in the different device structures. Our results demonstrate that grain boundaries act as dominant defects contributing to increased noise intensity under high forward bias. Statistical analysis of voltage noise spectral density across multiple samples, supported by Raman spectroscopy, reveals that hydrogen-related defects significantly contribute to 1/f noise in the linear region of the junction’s current–voltage characteristics. This study provides the first in-depth insight into the impact of h-BN interlayers on low-frequency noise in graphene/Si heterojunctions. Full article

Partial discharge (PD) is not only a critical indicator but also a major accelerating factor of insulation degradation in power transformers. Accurate identification of PD types is essential for diagnosing insulation defects and locating faults in transformers. Traditional methods based on phase-resolved partial discharge (PRPD) patterns typically rely on expert interpretation and manual feature extraction, which are increasingly being supplanted by Convolutional Neural Networks (CNNs) due to their ability to automatically extract features and deliver high classification accuracy. However, the inherent subtlety and diversity of characteristic differences among PRPD patterns, coupled with substantial noise resulting from complex electromagnetic interference, present significant hurdles to achieving accurate identification. This paper proposes a transformer partial discharge identification method based on Deep Residual Shrinkage Network (DRSN) to address these challenges. The method integrates dual-path feature extraction to capture both local and global features, incorporates a channel-domain adaptive soft-thresholding mechanism to effectively suppress noise interference, and utilizes the Focal Loss function to enhance the model’s attention to hard-to-classify samples. To validate the proposed method, given the scarcity of diverse real-world transformer PD data, an experimental platform was utilized to generate and collect PD data by artificially simulating various discharge defect models, including tip discharge, surface discharge, air-gap discharge and floating discharge. Data diversity was then enhanced through sample augmentation and noise simulation, to minimize the gap between experimental data and real-world on-site data. Experimental results demonstrate that the proposed method achieves superior partial discharge recognition accuracy and strong noise robustness on the experimental dataset. For future work, it is essential to collect more real transformer PD data to further validate and strengthen the model’s generalization capability, thereby ensuring its robust performance and applicability in practical scenarios. Full article