Search Results (77)

The intelligent transformation of power systems necessitates robust insulator condition detection to ensure grid safety. Existing methods, primarily reliant on manual inspection or conventional image processing, suffer significantly degraded target identification and detection efficiency under extreme weather conditions such as heavy snowfall. To address this challenge, this paper proposes an enhanced YOLO11s detection framework integrated with image restoration technology, specifically targeting insulator defect identification in snowy environments. First, data augmentation and a FocalNet-based snow removal algorithm effectively enhance image resolution under snow conditions, enabling the construction of a high-quality training dataset. Next, the model architecture incorporates a dynamic snake convolution module to strengthen the perception of tubular structural features, while the MPDIoU loss function optimizes bounding box localization accuracy and recall. Comparative experiments demonstrate that the optimized framework significantly improves overall detection performance under complex weather compared to the baseline model. Furthermore, it exhibits clear advantages over current mainstream detection models. This approach provides a novel technical solution for monitoring power equipment conditions in extreme weather, offering significant practical value for ensuring reliable grid operation. Full article

This study focuses on the susceptibility and surface degradation of low-alloy carbon steel 42CrMo4 to corrosion and cavitation erosion, as this steel is widely used in marine environments with aggressive chemical species and harsh conditions. Due to its high strength and fatigue resistance, 42CrMo4 steel is often employed in offshore mechanical components such as shafts and fasteners as well as crane parts in ports and harbors. Various experimental methods, including corrosion and cavitation tests, were used to assess the steel’s surface integrity under extreme conditions. Surface changes were monitored using modern analytical tools for precise assessments, including image and morphological analyses, to quantify degradation levels and specific parameters of defects induced by corrosion and cavitation. Non-destructive techniques such as optical microscopy (OM), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and image analysis software were employed for the quantitative assessment of morphological parameters and elemental analysis. EDS analysis revealed changes in elemental composition, indicating corrosion products that caused significant mass loss and defect formation, with degradation increasing over time. The average corrosion rate of 42CrMo4 steel in a 3.5% NaCl solution reached a peak value of 0.846 mm/year after 120 days of exposure. Cavitation erosion behavior was measured based on mass loss, indicating the occurrence of different cavitation periods, with the steady-state period achieved after 60 min. The number of formed pits increased until 120 min, after which it decreased slightly. This indicates that a time frame of 120 min was identified as significant for changes in the mechanism of pit formation. Specifically, up to 120 min, pit formation was the dominant mechanism of cavitation erosion, while after that, as the number of pits slightly declined, the growth and merging of formed pits became the dominant mechanism. The cavitation erosion tests showed mass loss and mechanical damage, characterized by the formation of pits and cavities. The findings indicate that the levels of surface degradation were higher for corrosion than for cavitation. The presented approach also provides an assessment of the degradation mechanisms of 42CrMo4 steel exposed to corrosive and cavitation conditions. Full article

A crucial task in industrial quality control, especially in the food and agriculture sectors, is the quick and precise estimation of an object’s volume. This study combines cutting-edge machine learning and computer vision techniques to provide a comprehensive, non-destructive method for predicting orange volume. We created a reliable pipeline that employs top and side views of every orange to estimate four important dimensions using a calibrated marker. These dimensions are then fed into a machine learning model that has been fine-tuned. Our method uses a range of engineered features, such as complex surface-area-to-volume ratios and new shape-based descriptors, to go beyond basic geometric formulas. Based on a dataset of 150 unique oranges, we show that the Stacking Regressor performs significantly better than other single-model benchmarks, including the highly tuned LightGBM model, achieving an $R^2$ score of 0.971. Because of its reliance on basic physical characteristics, the method is extremely resilient to the inherent variability in fruit and may be used with a variety of produce types. Because it allows for the real-time calculation of density (mass over volume) for automated defect detection and quality grading, this solution is directly applicable to a factory sorting environment. Full article

As automated equipment in PCB manufacturing becomes increasingly reliant on precision hot-air ovens, ensuring operational stability and reducing downtime have become critical challenges. Existing anomaly detection methods, such as Support Vector Machines (SVMs), Deep Neural Networks (DNNs), and Long Short-Term Memory (LSTM) Networks, struggle with high-dimensional dynamic data, leading to inefficiencies and overfitting. To address these issues, this study proposes an innovative anomaly detection system specifically designed for fault diagnosis in PCB hot-air ovens. The motivation is to improve accuracy and efficiency while adapting to dynamic changes in the manufacturing environment. The core innovation lies in the introduction of the Adaptive Temporal Feature Map (ATFM), which dynamically extracts and adjusts key temporal features in real time. By combining ATFM with Bi-Directional Dimensionality Reduction (BDDR) and eXtreme Gradient Boosting (XGBoost), the system effectively handles high-dimensional data and adapts its parameters based on evolving data patterns, significantly enhancing fault detection accuracy and efficiency. The experimental results show a fault prediction accuracy of 99.33%, greatly reducing machine downtime and product defects compared to traditional methods. Full article

Accumulative rolling bonding (ARB) Cu/Nb nanolaminates have been widely observed to exhibit unique and large numbers of interface-based plasticity mechanisms, and these have been associated with the many extraordinary properties of the material system, especially resistances in extreme engineering environments (mechanical/pressure, thermal, irradiation, etc.) and ability to self-heal defects (microstructural, as well as radiation-induced). Recently, anisotropy in the interface shearing mechanisms in the material system has been observed and much discussed. The Cu/Nb nanolaminates appear to shear on the interface planes to a much larger extent in the transverse direction (TD) than in the rolling direction (RD). Related to that, in this present study we observe interface rotation in Cu/Nb ARB nanolaminates under constrained and unconstrained loading conditions. Although the primary driving force for interface shearing was expected only in the RD, additional shearing in the TD was observed. This is significant as it represents an interface rotation, while there was no external rotational driving force. First, we observed interface rotation in in situ rectangular micropillar compression experiments, where the interface is simply sheared in one particular direction only, i.e., in the RD. This is rather unexpected as, in rectangular micropillar compression, there is no possibility of extra shearing or driving force in the perpendicular direction due to the loading conditions. This motivated us to subsequently perform in situ microbeam bending experiments (microbeam with a pre-made notch) to verify if similar interface rotation could also be observed in other loading modes. In the beam bending mode, the notch area was primarily under tensile stress in the direction of the beam longitudinal axis, with interfacial shear also in the same direction. Hence, we expect interface shearing only in that direction. We then found that interface rotation was also evident and repeatable under certain circumstances, such as under an offset loading. As this behaviour was consistently observed under two distinct loading modes, we propose that it is an intrinsic characteristic of Cu/Nb interfaces (or FCC/BCC interfaces with specific orientation relationships). This interface rotation represents another interface-based or interface-mediated plasticity mechanism at the nanoscale with important potential implications especially for design of metallic thin films with extreme stretchability and other emerging applications. Full article

Silicon-based MEMS devices are essential in extreme radiation environments but suffer progressive reliability degradation from irradiation-induced defects. Here, the generation, aggregation, and clustering of defects in single-crystal silicon were systematically investigated through molecular dynamics (MD) simulations via employing a hybrid Tersoff–ZBL potential that was validated by nanoindentation and transmission electron microscopy. The influences of the primary knock-on atom energy, temperature, and pre-strain state on defect evolution were quantified in detail. Frenkel defects were found to cause a linear reduction in the Young’s modulus and a nonlinear decline in thermal conductivity via enhanced phonon scattering. To link atomic-scale damage with device-level performance, MD-predicted modulus degradation was incorporated into finite element (FE) models of a sensing diaphragm. The FE analysis revealed that modulus reductions result in nonlinear increases in deflection and stress concentration, potentially impairing sensing accuracy. This integrated MD–FE framework establishes a robust, physics-based approach for predicting and mitigating irradiation damage in silicon-based MEMS operating in extreme environments. Full article

As emergent material candidates for extreme environments, refractory high-entropy alloys (HEAs) or refractory multi-principal-element alloys (RMPEAs) comprising refractory metals feature qualities such as high radiation tolerance, corrosion resistance, and mechanical strength. A set of MoNbTi-based RMPEA samples with Al, Cr, V, and Zr additions are prepared by spark plasma sintering and investigated for their response to irradiation using 10 MeV Si⁺ ions with a dose of $1.43\times {10}^{15}$ ions/cm². Positron annihilation spectroscopy and transmission electron microscopy are employed as atomic- and meso- scale techniques to reveal how chemical complexity, nanotwinning, and phase fractions play an important role in radiation-induced defect accumulation and damage tolerance. The study provides experimental evidence of nanotwinning acting as an effective sink for radiation-induced point defects. Full article

High-entropy alloys (HEAs) exhibit superior properties for extreme environments, yet the effects of laser scanning speed on the microstructure and performance of laser-clad NiAlNbTiV HEA coatings remain unclear. This study systematically investigates NiAlNbTiV coatings on 316 stainless steel fabricated at scanning speeds of 800–1100 mm/min via laser cladding. Characterizations via XRD, SEM/EDS, microhardness testing, high-temperature wear testing, and electrochemical measurements reveal that increasing scanning speed enhances the cooling rate, promoting γ-(Ni, Fe) solid solution formation, intensifying TiV peaks, and reducing Fe-Nb intermetallics. Higher speeds refine grains and needle-like crystal distributions but introduce point defects and cracks at 1100 mm/min. Microhardness decreases from 606.2 HV (800 mm/min) to 522.4 HV (1100 mm/min). The 800 mm/min coating shows optimal wear resistance (wear volume: 0.0117 mm³) due to dense eutectic hard phases, while higher speeds degrade wear performance via increased defects. Corrosion resistance follows a non-linear trend, with the 900 mm/min coating achieving the lowest corrosion current density (1.656 μA·cm⁻²) due to fine grains and minimal defects. This work provides parametric optimization guidance for laser-clad HEA coatings in extreme-condition engineering applications. Full article

As a kind of novel binderless composite, WC-cBN-SiC_w composite possesses significant potential value in special sealing components and high-pressure medium nozzles. However, under severe wear and corrosion conditions, the surface defects caused by friction will be accelerated to become a crack source in aggressive environments. Because of the intrinsic brittleness of WC cemented carbide, its strength is extremely sensitive to local surface damage. Therefore, the influence of applied load (10 N, 20 N, 40 N and 60 N) on its tribological behavior was studied. Meanwhile, the impact of corrosion resistance of WC-cBN-SiC_w composite on surface damage induced by friction was further investigated. Full article

Electrical sealing covers are widely used in various industrial equipment, where the quality of their metal-painted surfaces directly affects product appearance and long-term reliability. Micro-defects such as pores, particles, scratches, and uneven paint coatings can compromise protective performance during manufacturing. In the rapidly growing new energy vehicle (NEV) industry, battery charging-port sealing covers are critical components, requiring precise defect detection due to exposure to harsh environments, like extreme weather and dust-laden conditions. Even minor defects can lead to water ingress or foreign matter accumulation, affecting vehicle performance and user safety. Conventional manual or rule-based inspection methods are inefficient, and the existing deep learning models struggle with detecting minor and subtle defects. To address these challenges, this study proposes EIM-YOLO, an improved object detection framework for the automated detection of metal-painted surface defects on electrical sealing covers. We propose a novel lightweight convolutional module named C3PUltraConv, which reduces model parameters by 3.1% while improving mAP₅₀ by 1% and recall by 3.2%. The backbone integrates RFAConv for enhanced feature perception, and the neck architecture uses an optimized BiFPN-concat structure with adaptive weight learning for better multi-scale feature fusion. Experimental validation on a real-world industrial dataset collected using industrial cameras shows that EIM-YOLO achieves a precision of 71% (an improvement of 3.4%), with mAP₅₀ reaching 64.8% (a growth of 2.6%), and mAP_50–95 improving by 1.2%. Maintaining real-time detection capability, EIM-YOLO significantly outperforms the existing baseline models, offering a more accurate solution for automated quality control in NEV manufacturing. Full article

Developing structural materials capable of maintaining integrity under extreme irradiation conditions is a cornerstone challenge for advancing sustainable nuclear energy technologies. The complexity and severity of radiation-induced microstructural changes—spanning multiple length and timescales—pose significant hurdles for purely experimental approaches. This review critically evaluates recent advancements in atomistic modeling, emphasizing its transformative potential to decipher fundamental mechanisms driving microstructural evolution in irradiated alloys. Atomistic simulations, such as molecular dynamics (MD), have successfully unveiled initial defect formation processes at picosecond scales. However, the inherent temporal limitations of conventional MD necessitate advanced methodologies capable of exploring slower, thermally activated defect kinetics. We specifically traced the development of powerful potential energy landscape (PEL) exploration algorithms, which enable the simulation of high-barrier, rare events of defect evolution processes that govern long-term material degradation. The review systematically examines point defect behaviors in various crystal structures—BCC, FCC, and HCP metals—and elucidates their characteristic defect dynamics, respectively. Additionally, it highlights the pronounced effects of chemical complexity in concentrated solid-solution alloys and high-entropy alloys, notably their sluggish diffusion and enhanced defect recombination, underpinning their superior radiation tolerance. Further, the interaction of extended defects with mechanical stresses and their mechanistic implications for material properties are discussed, highlighting the critical interplay between thermal activation and strain rate in defect evolution. Special attention is dedicated to the diverse mechanisms of dislocation–obstacle interactions, as well as the behaviors of metastable grain boundaries under far-from-equilibrium environments. The integration of data-driven methods and machine learning with atomistic modeling is also explored, showcasing their roles in developing quantum-accurate potentials, automating defect analysis, and enabling efficient surrogate models for predictive design. This comprehensive review also outlines future research directions and fundamental questions, paving the way toward autonomous materials’ discovery in extreme environments. Full article

This study explores the development of a novel composite coating system combining the high hardness of WC and thermal conductivity of Cu, employing the plasma transfer arc welding method under ambient conditions. Utilizing an advanced welding approach, the work investigates microstructural evolution and phase formation in a WC-Cu-based coating applied to a mild steel substrate. Emphasis is placed on understanding the solidification behaviour and its influence on defects, microstructural refinement, and carbide formation. The study provides insights into the interactions between coating constituents and the underlying substrate under controlled thermal conditions. These findings demonstrate the potential for producing functionally graded coatings tailored for demanding wear and heat dissipation applications. The approach offers a pathway for enhancing the durability and performance of steel components in extreme service environments. Full article

The extended operation of high-speed railways has led to an increased incidence of tunnel lining defects, with falling debris posing a significant safety threat. Within tunnels, single-train passage and trains-crossing events constitute the most frequent operational scenarios, both generating extreme aerodynamic environments that alter debris trajectories from free fall. To systematically investigate the aerodynamic differences and underlying mechanisms governing falling debris behavior under these two distinct conditions, a three-dimensional computational fluid dynamics (CFD) model (debris–air–tunnel–train) was developed using an improved delayed detached eddy simulation (IDDES) turbulence model. Comparative analyses focused on the translational and rotational motions as well as the aerodynamic load coefficients of the debris in both single-train and trains-crossing scenarios. The mechanisms driving the changes in debris aerodynamic behavior are elucidated. Findings reveal that under single-train operation, falling debris travels a greater distance compared with trains-crossing conditions. Specifically, at train speeds ranging from 250–350 km/h, the average flight distances of falling debris in the X and Z directions under single-train conditions surpass those under trains crossing conditions by 10.3 and 5.5 times, respectively. At a train speed of 300 km/h, the impulse of C_Fx and C_Fz under single-train conditions is 8.6 and 4.5 times greater than under trains-crossing conditions, consequently leading to the observed reduction in flight distance. Under the conditions of trains crossing, the falling debris is situated between the two trains, and although the wind speed is low, the flow field exhibits instability. This is the primary factor contributing to the reduced flight distance of the falling debris. However, it also leads to more pronounced trajectory deviations and increased speed fluctuations under intersection conditions. The relative velocity (CRV) on the falling debris surface is diminished, resulting in smaller-scale vortex structures that are more numerous. Consequently, the aerodynamic load coefficient is reduced, while the fluctuation range experiences an increase. Full article

This study demonstrates the successful fabrication of high-performance reaction-bonded silicon carbide (RBSC) ceramics through an optimized liquid silicon infiltration (LSI) process employing multi-modal SiC particle gradation and nano-carbon black (0.6 µm) additives. By engineering porous preforms with hierarchical SiC distributions and tailored carbon sources, the resulting ceramics achieved a compressive strength of 2393 MPa and a flexural strength of 380 MPa, surpassing conventional RBSC systems. Microstructural analyses revealed homogeneous β-SiC formation and crack deflection mechanisms as key contributors to mechanical enhancement. Ultrafine SiC particles (0.5–2 µm) refined pore architectures and mediated capillary dynamics during infiltration, enabling nanoscale dispersion of residual silicon phases and minimizing interfacial defects. Compared to coarse-grained counterparts, the ultrafine SiC system exhibited a 23% increase in compressive strength, attributed to reduced sintering defects and enhanced load transfer efficiency. This work establishes a scalable strategy for designing RBSC ceramics for extreme mechanical environments, bridging material innovation with applications in high-stress structural components. Full article

This paper presents the synthesis and characterization of Ni-TiN@CN nanocomposites fabricated via arc discharge, followed by dopamine polymerization and pyrolysis. The cubic morphology of the Ni-TiN cores and uniform CN encapsulation were confirmed by structural analyses. Electromagnetic evaluations revealed that the CN shell thickness critically influenced the dielectric dispersion, polarization relaxation and conductive loss. The optimal sample (Ni-TiN@CN-3) achieved a minimum reflection loss of −42.05 dB at 4.06 GHz. The incorporation of magnetic Ni particles introduced a magnetic loss mechanism, while the multiple intrinsic defects within the heterogeneous structure synergistically generated defect dipole polarization and conductive loss. The strategic addition of Ni facilitated the construction of heterogeneous interfaces, which achieved enhanced interface polarization effects. The effective absorption bandwidth (≤−10 dB) reached 14.9 GHz, while the effective absorption bandwidth (≤−20 dB) achieved 6.5 GHz. The optimized CN layer facilitated a synergistic interplay between the dielectric loss and magnetic loss, which ensured balanced impedance matching and attenuation, as well as enhanced electromagnetic wave dissipation. This integrated optimization ultimately endowed the material with exceptional microwave absorption performance through an effective electromagnetic energy conversion. This work highlights Ni-TiN@CN nanocomposites as promising candidates for high-performance microwave absorbers in extreme environments. Full article