Search Results (143)

Silicon nitride (Si₃N₄) ceramic bearings inevitably contain crack-like defects, yet their compressive capacity degradation and crack-driven failure mechanisms remain unclear. This study proposes a discrete element method (DEM) numerical framework within PFC2D to simulate a bearing containing a single prefabricated crack. First, a bearing DEM model was established and calibrated to reproduce the compressive mechanical response. Then, particle deletion introduced controllable central cracks in the ball and raceway with prescribed inclination angles. Finally, displacement-controlled compression-splitting simulations, serving as a surrogate for a quasi-static overload scenario relevant to quality screening, tracked crack initiation, propagation, and failure modes; under a fixed raceway-crack inclination, crack length was varied to quantify size effects. Results show that a single crack markedly reduces compressive strength. Failure progresses through elastic deformation, crack propagation, and final fracture, with cracks initiating at stress concentrators near crack tips. Crack inclination significantly regulates capacity: raceway cracks are most detrimental near 45°, while ball cracks exhibit an overall decrease in initiation and peak stresses with increasing inclination (with local non-monotonicity). Crack length has a stronger weakening effect than inclination, with accelerated capacity loss beyond 0.3 mm and a pronounced drop in initiation stress beyond 0.6 mm. The framework enables controllable defect parametrization and micro-scale failure interpretation for defect sensitivity assessment under compressive overload. Thus, this study focuses on simulating monotonic fracture events to elucidate fundamental defect–property relationships, which provides a foundation distinct from the prediction of rolling contact fatigue life under cyclic service conditions. Full article

Edge cracks in hot-rolled sheets of industrial grain-oriented electrical steel significantly affect the yield rate and pose substantial challenges to cold rolling fabrication. Eliminating such structural defects through hot rolling requires a thorough understanding of their formation mechanism. This study investigates the formation mechanism of edge cracks in hot-rolled sheets, which are characterized by coarse strip-like grains with typical thicknesses ranging from 20 μm to 100 μm. Coarse, strip-shaped grains have low fracture stress, which is the cause of edge cracks. They originate from abnormally developed columnar grains in continuous casting slabs after reheating, which is unavoidable in industrial large-scale production. Inadequate fragmentation and insufficient recrystallization during rough rolling result in residual coarse grains of intermediate slabs, and their preferential deformation and outward protrusion lead to the formation of grooves. In the subsequent finishing rolling process, deformed coarse grains near the grooves undergo further elongation, developing into distinct strip-like structures. Based on the above mechanistic understanding, the edge microstructure under various rolling parameters was investigated, and targeted improvement measures for edge cracks were proposed. It is concluded that the edge quality can be significantly enhanced through increasing the total width reduction, additional rough rolling passes, and the implementation of edge heating during rough rolling. Quantitative analysis demonstrates that increasing the rolling passes from D to E significantly reduces the fraction of band structure from 64% to 48% and the average width of elongated grains from 43.5 μm to 38.4 μm. Full article

The rapid urbanization of coastal regions has intensified the demand for durable underground infrastructure like shield tunnels, where reinforced concrete (RC) structures are critical yet susceptible to long-term degradation in marine environments. This study develops an integrated machine vision-based framework for assessing the long-term durability of RC in marine shield tunnels by synergistically combining point cloud analysis and deep learning-based damage recognition. The methodology involves preprocessing tunnel point clouds to extract the centerline and cross-sections, enabling the quantification of geometric deformations, including segment misalignment and elliptical distortion. Concurrently, an advanced YOLOv8 model is employed to automatically identify and classify surface corrosion damages—specifically water leakage, cracks, and spalling—from images, achieving high detection accuracies (e.g., 95.6% for leakage). By fusing the geometric indicators with damage metrics, a quantitative risk scoring system is established to evaluate structural durability. Experimental results on a real-world tunnel segment demonstrate the framework’s effectiveness in correlating surface defects with underlying geometric irregularities. This integrated approach offers a data-driven solution for the continuous health monitoring and residual life prediction of RC tunnel linings in marine conditions, bridging the gap between visual inspection and structural performance assessment. Full article

The rapid advancement of high-performance electronics has intensified the demand for wide-bandgap semiconductor materials capable of operating under high-power and high-temperature conditions. Among these, silicon carbide (SiC) has emerged as a leading candidate due to its superior thermal conductivity, chemical stability, and mechanical strength. However, the high cost and complexity of SiC wafer fabrication, particularly in slicing and exfoliation, remain significant barriers to its widespread adoption. Conventional methods such as wire sawing suffer from considerable kerf loss, surface damage, and residual stress, reducing material yield and compromising wafer quality. Additionally, techniques like smart-cut ion implantation, though capable of enabling thin-layer transfer, are limited by long thermal annealing durations and implantation-induced defects. To overcome these limitations, ultrafast laser-based processing methods, including laser slicing and stealth dicing (SD), have gained prominence as non-contact, high-precision alternatives for SiC wafer exfoliation. This review presents the current state of the art and recent advances in laser-based precision SiC wafer exfoliation processes. Laser slicing involves focusing femtosecond or picosecond pulses at a controlled depth parallel to the beam path, creating internal damage layers that facilitate kerf-free wafer separation. In contrast, stealth dicing employs laser-induced damage tracks perpendicular to the laser propagation direction for chip separation. These techniques significantly reduce material waste and enable precise control over wafer thickness. The review also reports that recent studies have further elucidated the mechanisms of laser–SiC interaction, revealing that femtosecond pulses offer high machining accuracy due to localized energy deposition, while picosecond lasers provide greater processing efficiency through multipoint refocusing but at the cost of increased amorphous defect formation. The review identifies multiphoton ionization, internal phase explosion, and thermal diffusion key phenomena that play critical roles in microcrack formation and structural modification during precision SiC wafer laser processing. Typical ultrafast-laser operating ranges include pulse durations from 120–450 fs (and up to 10 ps), pulse energies spanning 5–50 µJ, focal depths of 100–350 µm below the surface, scan speeds ranging from 0.05–10 mm/s, and track pitches commonly between 5–20 µm. In addition, the review provides quantitative anchors including representative wafer thicknesses (250–350 µm), typical laser-induced crack or modified-layer depths (10–40 µm and extending up to 400–488 µm for deep subsurface focusing), and slicing efficiencies derived from multi-layer scanning. The review concludes that these advancements, combined with ongoing progress in ultrafast laser technology, represent research opportunities and challenges in transformative shifts in SiC wafer fabrication, offering pathways to high-throughput, low-damage, and cost-effective production. This review highlights the comparative advantages of laser-based methods, identifies the research gaps, and outlines the challenges and opportunities for future research in laser processing for semiconductor applications. Full article

Photovoltaic systems (PV) are increasingly recognized as fundamental to the worldwide adoption of renewable energy technologies. Nonetheless, the efficiency and longevity of solar panels can be compromised by various anomalies, ranging from physical defects to environmental impacts. Early and accurate detection of these anomalies is crucial for maintaining optimal performance and preventing significant energy losses. This study presents SolarAttnNet, a novel convolutional neural network (CNN) architecture with integrated channel and spatial attention mechanisms for solar panel anomaly detection. The proposed model addresses the critical need for automated detection systems, which are crucial for maintaining energy production efficiency and optimizing maintenance. This approach leverages attention mechanisms that emphasize the most relevant features within thermal and visual imagery, improving detection accuracy across multiple anomaly types. SolarAttnNet is evaluated on three distinct solar panel datasets, demonstrating its effectiveness through comprehensive ablation studies that isolate the contribution of each architectural component. Experimental results show that SolarAttnNet achieves superior performance compared to state-of-the-art methods, with accuracy improvements of 3.9% on the PV Systems-AD dataset (94.2% vs. 90.3%), 3.6% on the InfraredSolarModules dataset (92.1% vs. 88.5%), and 3.5% on the RoboflowAnomalies dataset (89.7% vs. 86.2%) compared to baseline ResNet-50. For challenging subtle anomalies like cell cracks and PID, the proposed model demonstrates even more significant improvements with F1-score gains of 4.8% and 5.4%, respectively. Ablation studies reveal that the channel attention mechanism contributes a 2.6% accuracy improvement while spatial attention adds 2.3% across datasets. This work contributes to advancing automated inspection technologies for renewable energy infrastructure, supporting more efficient maintenance protocols and ultimately enhancing solar energy production. Full article

In this study, a small core diameter single mode fiber laser was applied to weld an 8 mm thick plate of 6061-T6 aluminum alloy. The microstructural evolution and mechanical properties of the laser welded aluminum alloy specimens were investigated in detail. The results indicated that fully penetrated welded specimens, free of welding defects like porosity, melt sagging, and hot cracking could be achieved by optimizing the processing parameters through response surface methodology. The upper part of the fusion zone consisted mainly of fine equiaxed dendrites, with secondary dendrite arm spacing (SDAS) of approximately 3–5 μm. While the lower region of the fusion zone exhibited pronounced microstructural coarsening, made up mostly of coarse columnar grains, along with some localized equiaxed grains, and an SDAS ranging from 8 to 12 μm. Both the fusion zone and heat affected zone (HAZ) were characterized by a “softened” hardness profile. The fusion zone featured a narrow region with the lowest microhardness across the welded joint with the microhardness value reducing to ~72% of the base metal (BM). Meanwhile, the microhardness of the HAZ was ~87.4% of the BM. The ultimate tensile strength of laser welded specimens was ~243.6 MPa, amounting to approximately 78.3% of the base metal. This study provides a fresh approach for welding medium-thick aluminum alloy plate using a high-quality laser beam, even at the kilowatt level with a fiber laser, and it shows a strong promise for applications in light-alloy manufacturing sectors such as automotive, rail transportation, aerospace, and beyond. Full article

Precise perception of subtle anomalies in power equipment—such as insulator cracks, conductor corrosion, or foreign intrusions—is vital for ensuring the reliability of smart grids. However, foundational vision-language models (VLMs) like CLIP exhibit poor domain transfer and fail to capture minute defect semantics. We propose VisPower, a curriculum-guided multimodal alignment framework that progressively enhances fine-grained perception through two training stages: (1) Semantic Grounding, leveraging 100 K long-caption pairs to establish a robust linguistic-visual foundation, and (2) Contrastive Refinement, using 24 K region-level and hard-negative samples to strengthen discrimination among visually similar anomalies. Trained on our curated PowerAnomalyVL dataset, VisPower achieves an 18.4% absolute gain in zero-shot retrieval accuracy and a 16.8% improvement in open-vocabulary defect detection (OV-DD) over strong CLIP baselines. These results demonstrate the effectiveness of curriculum-based multimodal alignment for high-stakes industrial anomaly perception. Full article

Cracks were found at the gate of the 430Cb ferritic stainless steel exhaust system jet base produced by investment casting. In this paper, the cracks of failed stainless steel castings were comprehensively analyzed by means of macroscopic inspection, laser confocal microscopy, field emission scanning electron microscopy, electron backscatter diffraction, X-ray diffractometer, ProCAST (version 2018, ESI Group, Paris, France) simulation and Thermo-Calc (TCFE10 database, 2022a, Thermo-Calc Software AB, Solna, Sweden) thermodynamic calculation. It can be concluded that all the cracks originate from the gate on the surface of the casting, and the fracture surface shows brittle intergranular characteristics, which can be determined as cold cracks. The formation of cold cracks can be attributed to the fact that the local stress generated during cooling after the casting solidifies exceeds the strength limit of the material itself. As the gate is the final solidification zone, shrinkage is limited and stress is concentrated. The grains are coarse, and the microstructure defects such as shrinkage porosity, pores and needle-like NbC further weaken the plasticity of the grain boundaries, promoting the crack to propagate along the direction of the maximum principal stress. The uneven cooling rate and shell constraint during the investment casting process make it difficult to release stress, and the existence of microstructure defects are the fundamental causes of crack generation. Full article

Structure detection (SD) has emerged as a critical technology for ensuring the safety and longevity of infrastructure, particularly in housing and civil engineering. Traditional SD methods often rely on manual inspections, which are time-consuming, labor-intensive, and prone to human error, especially in complex environments such as dense urban settings or aging buildings with deteriorated materials. Recent advances in autonomous systems—such as Unmanned Aerial Vehicles (UAVs) and climbing robots—have shown promise in addressing these limitations by enabling efficient, real-time data collection. However, challenges persist in accurately detecting and analyzing structural defects (e.g., masonry cracks, concrete spalling) amidst cluttered backgrounds, hardware constraints, and the need for multi-scale feature integration. The integration of machine learning (ML) and deep learning (DL) has revolutionized SD by enabling automated feature extraction and robust defect recognition. For instance, RepConv architectures have been widely adopted for multi-scale object detection, while attention mechanisms like TAM (Technology Acceptance Model) have improved spatial feature fusion in complex scenes. Nevertheless, existing works often focus on singular sensing modalities (e.g., UAVs alone) or neglect the fusion of complementary data streams (e.g., ground-based robot imagery) to enhance detection accuracy. Furthermore, computational redundancy in multi-scale processing and inconsistent bounding box regression in detection frameworks remain underexplored. This study addresses these gaps by proposing a generalized safety inspection system that synergizes UAV and stair-climbing robot data. We introduce a novel multi-scale targeted feature extraction path (Rep-FasterNet TAM block) to unify automated RepConv-based feature refinement with dynamic-scale fusion, reducing computational overhead while preserving critical structural details. For detection, we combine traditional methods with remote sensor fusion to mitigate feature loss during image upsampling/downsampling, supported by a structural model GIOU [Mathematical Definition: GIOU = IOU − (C − U)/C] that enhances bounding box regression through shape/scale-aware constraints and real-time analysis. By siting our work within the context of recent reviews on ML/DL for SD, we demonstrate how our hybrid approach bridges the gap between autonomous inspection hardware and AI-driven defect analysis, offering a scalable solution for large-scale housing safety assessments. In response to challenges in detecting objects accurately during housing safety assessments—including large/dense objects, complex backgrounds, and hardware limitations—we propose a generalized inspection system leveraging data from UAVs and stair-climbing robots. To address multi-scale feature extraction inefficiencies, we design a Rep-FasterNet TAM block that integrates RepConv for automated feature refinement and a multi-scale attention module to enhance spatial feature consistency. For detection, we combine dynamic-scale remote feature fusion with traditional methods, supported by a structural GIOU model that improves bounding box regression through shape/scale constraints and real-time analysis. Experiments demonstrate that our system increases masonry/concrete assessment accuracy by 11.6% and 20.9%, respectively, while reducing manual drawing restoration workload by 16.54%. This validates the effectiveness of our hybrid approach in unifying autonomous inspection hardware with AI-driven analysis, offering a scalable solution for SD in housing infrastructure. Full article

Residual stress plays a critical role in the durability and structural integrity of steel rolls and bars. Proper analysis helps prevent defects like warping or cracking, ensuring the steel meets quality standards and performs reliably in critical applications. This paper presents a methodology for analysing residual stresses using electromagnetic acoustic transducer (EMAT) based nonlinear ultrasonics. It compares its effectiveness with established techniques such as X-ray diffraction (XRD) and coercive force measurements. The results demonstrate that nonlinear ultrasonics provides more detailed insights into stress distribution, particularly in subsurface regions where traditional methods like XRD face limitations. It also shows good sensitivity to stress-induced microstructural variations than coercive force measurements. This research study is the first to perform a comparative analysis using XRD, EMAT, and coercive force techniques on industrial samples, followed by the implementation of EMAT nonlinear technology at an industrial production site. The findings indicate a positive trend observed in XRD and coercive force results, and those from nonlinear ultrasonics, further validating its accuracy. Moreover, the technology has been successfully applied in steel manufacturing industries through the project named STEEL components assessment using a novel non-destructive residual stress ultrasonic technology (STEELAR), funded by the Research Fund for Coal and Steel (RFCS). These findings underscore the potential of nonlinear ultrasonics as a powerful, fast and complementary tool for comprehensive residual stress monitoring in steel components, enhancing both theoretical understanding and practical industrial application. Full article

By alternately depositing hydrogen-free amorphous carbon (a-C) and hydrogenated amorphous carbon (a-C:H) nanolayers on HSLA-100 steel through arc-ion plating, multilayer diamond-like carbon (DLC) architectures were engineered, with the modulation period adjusted from 1 to 10 cycles. SEM and Raman spectroscopy served as the analytical tools for characterizing the microstructure. For assessing key functional behaviors, nanoindentation was used to test mechanical properties, dry-sliding tribometry and in-situ tribocorrosion tests targeted tribological and tribocorrosion performance, and polarization tests focused on corrosion resistance. Introducing C₂H₂ increased the sp³ fraction and hardness relative to pure a-C. The ten-period film (S5) yielded the highest H/E (0.0767) and H³/E² (0.171), reflecting the best hardness–toughness synergy. All coatings lowered the dry friction coefficient to 0.08–0.10 and cut wear by more than 1 order of magnitude versus the substrate; the ten-period film (S5) showed the minimum dry wear rate (1.39 × 10⁻⁷ mm³·N⁻¹·m⁻¹) and tribocorrosion wear rate (4.53 × 10⁻⁷ mm³·N⁻¹·m⁻¹) in 3.5 wt% NaCl. The superior performance is due to interlayer interfaces that dissipate stresses, arrest crack propagation, and block electrolyte ingress through defects. These findings indicate that the rational stacking of a-C/a-C:H significantly improves the tribological and tribocorrosion resistance of HSLA-100, providing a reliable protective approach for components used in marine services. Full article

Metal components such as rolling bearings are prone to wear, cracks, and defects in harsh environments and long-term use, leading to performance degradation and potential equipment failures. Therefore, detecting surface cracks and other defects in rolling bearings is of great significance for ensuring equipment reliability and safety. However, traditional signal decomposition methods like EEMD and FEEMD suffer from residual noise and mode mixing issues, while deconvolution algorithms such as CYCBD are sensitive to parameter settings and struggle in high-noise environments. To mitigate the susceptibility of fault signals to background noise interference, this paper proposes a fault feature extraction method based on fast complementary ensemble empirical mode decomposition (FCEEMD) and adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). Firstly, we propose FCEEMD, which effectively eliminates the residual noise of ensemble empirical mode decomposition (EEMD) and fast ensemble empirical mode decomposition (FEEMD) by introducing paired white noise with opposite signs, solving the problems of traditional decomposition methods that are greatly affected by noise, having large reconstruction errors, and being high time-consuming. Subsequently, a new intrinsic mode function (IMF) screening index based on correlation coefficients and energy kurtosis is developed to effectively mitigate noise influence and enhance the quality of signal reconstruction. Secondly, the ACYCBD model is constructed, and the hidden periodic frequency is detected by the enhanced Hilbert phase synchronization (EHPS) estimator, which significantly enhances the extraction effect of the real periodic fault features in the noise. Finally, instantaneous energy tracking of bearing fault characteristic frequency is achieved through Teager energy operator demodulation, thereby accurately extracting fault state features. The experiment shows that the proposed method accurately extracts the fault characteristic frequencies of 164.062 Hz for inner ring faults and 105.469 Hz for outer ring faults, confirming its superior accuracy and efficiency in rolling bearing fault diagnosis. Full article

The automated inspection of civil infrastructure with Unmanned Aerial Vehicles (UAVs) is hampered by the challenge of accurately segmenting multi-damage in high-resolution imagery. While foundational models like the Segment Anything Model (SAM) offer data-efficient segmentation, their effectiveness is constrained by prompting strategies, especially for geometrically complex defects. This paper presents a comprehensive comparative analysis of deep learning strategies to identify an optimal deep learning pipeline for segmenting cracks, efflorescences, and exposed rebars. It systematically evaluates three distinct end-to-end segmentation frameworks: the native output of a YOLO11 model; the Segment Anything Model (SAM), prompted by bounding boxes; and SAM, guided by a point-prompting mechanism derived from the detector’s probability map. Based on these findings, a final, optimized hybrid pipeline is proposed: for linear cracks, the native segmentation output of the SAHI-trained YOLO model is used, while for efflorescence and exposed rebar, the model’s bounding boxes are used to prompt SAM for a refined segmentation. This class-specific strategy yielded a final mean Average Precision (mAP50) of 0.593, with class-specific Intersection over Union (IoU) scores of 0.495 (cracks), 0.331 (efflorescence), and 0.205 (exposed rebar). The results establish that the future of automated inspection lies in intelligent frameworks that leverage the respective strengths of specialized detectors and powerful foundation models in a context-aware manner. Full article

This study investigates the uniaxial compression failure of magnesium-based wood-like material (MWM) prisms (100 × 100 × 300 mm³) using digital image correlation (DIC). The results revealed an average compressive strength of 8.76 MPa and a dominant failure mode with Y-shaped or inclined penetrating cracks. A novel piecewise constitutive model was established, combining a quartic polynomial and a rational fraction, demonstrating high fitting accuracy. Critically, the proportional limit was identified to be very low (20–35% of peak stress), attributed to early-stage damage from fiber–matrix interfacial defects. DIC analysis quantitatively distinguished dual crack initiation modes, pure mode I (occurring at ≈100% peak load) and mixed mode I/II (initiating earlier at 90.02% peak load), demonstrating that tensile shear coupling accelerates failure. These findings provide critical mechanistic insights and a reliable model for optimizing MWM in sustainable construction. Future work will explore the material’s behavior under multiaxial loading. Full article

Vibro-thermography is an effective nondestructive testing technique for detecting closed defects like cracks and delaminations through frictional heat generated under ultrasonic excitation. However, its accuracy is often reduced by standing wave patterns that create periodic temperature artifacts in non-defective areas, leading to false positives. To overcome this, we propose an image processing approach using 2D Fourier Transform (2D-FFT) to reduce SW-induced patterns in the frequency domain. This enhances defect visibility by suppressing unwanted heat signatures. The method is evaluated on a cracked PMMA plate and a hollow tube of the same material. Full article