Search Results (1,820)

Additive manufacturing (AM, also known as 3D printing) is a bottom–up process where variations in process conditions can significantly influence the quality and performance of the printed parts. Digital twin (DT) technology can measure process parameters and printed part characteristics in real-time, achieving online monitoring, analysis, and optimization of the AM process. Existing DT research on AM focuses on simulating the printing process and lacks real-time defect detection and twinning of actual printed objects, which hinders the timely detection and correction of defects. This study developed a DT system for fused deposition modeling (FDM) AM technology that not only accurately simulates the printing process but also performs real-time quality monitoring of the printed parts. A laser profilometer and industrial camera were integrated into the printer to detect and collect real-time morphological data on the printed object. The custom-developed DT software could convert the morphological data of the printed parts into a DT model. By comparing the DT model of the printed object with its three-dimensional model, defect detection of the printed parts was achieved, where the quality of the printed parts was evaluated using a defect percentage index. This study combines DT and AM to achieve process quality monitoring, demonstrating the potential of DT technology in reducing printing defects and improving the quality of printed parts. Full article

With the increasing demand for drag reduction, energy consumption reduction, and low weight in civil aircraft, high-precision microgroove preparation technology is being developed internationally to reduce wall friction resistance and save energy. Compared to mechanical processing, chemical etching, roll forming, and ultrafast laser processing, nanosecond lasers offer processing precision, high efficiency, and controllable thermal effects, enabling low-cost and high-quality preparation of microgrooves. However, the impact of nanosecond laser etching on the fatigue performance of substrate materials remains unclear, leading to controversy over whether high-precision shape control and fatigue performance enhancement in microgrooves can be achieved simultaneously. This has become a bottleneck issue that urgently needs to be addressed. This paper focuses on the current research status of nanosecond laser processing quality control for microgrooves and the research status of laser effects on enhancing the fatigue performance of substrate materials. It identifies the main existing issues: (1) how to induce surface residual compressive stress through the thermo-mechanical coupling effect of nanosecond lasers to suppress micro-defects while ensuring high-precision shape control of fixed microgrooves; and (2) how to quantify the regulation of nanosecond laser process parameters on residual stress distribution and fatigue performance in the microgroove area. To address these issues, this paper proposes a collaborative strategy for high-quality shape control and surface strengthening in fixed microgrooves, an analysis of multi-dimensional fatigue regulation mechanisms, and a new method for multi-objective process optimization. The aim is to control the geometric accuracy error of the prepared surface microgrooves within 5% and to enhance the fatigue life of the substrate by more than 20%, breaking through the technical bottleneck of separating “drag reduction design” from “fatigue resistance manufacturing”, and providing theoretical support for the integrated manufacturing of “drag reduction-fatigue resistance” in aircraft skins. Full article

In anomaly detection tasks, labeled defect data are often scarce. Unsupervised learning leverages only normal samples during training, making it particularly suitable for anomaly detection tasks. Among unsupervised methods, normalizing flow models have shown distinct advantages. They allow precise modeling of data distributions and enable direct computation of sample log-likelihoods. Recent work has largely focused on feature fusion strategies. However, most of the flow-based methods emphasize spatial information while neglecting the critical role of channel-wise features. To address this limitation, we propose GCAFlow, a novel flow-based model enhanced with a global context-aware channel attention mechanism. In addition, we design a hierarchical convolutional subnetwork to improve the probabilistic modeling capacity of the flow-based framework. This subnetwork supports more accurate estimation of data likelihoods and enhances anomaly detection performance. We evaluate GCAFlow on three benchmark anomaly detection datasets, and the results demonstrate that it consistently outperforms existing flow-based models in both accuracy and robustness. In particular, on the VisA dataset, GCAFlow achieves an image-level AUROC of 98.2% and a pixel-level AUROC of 99.0%. Full article

Structural adhesives or fixing glues are typically applied to larger components on printed circuit boards (PCBs) to increase mechanical stability and minimize damage from vibration. Existing work tends to focus on component placement verification and solder joint analysis, etc. However, the detection of structural adhesives remains largely unexplored. This paper proposes a vision-based method for detecting structural adhesive defects on PCBs. The method uses HSV color segmentation to extract PCB regions, followed by Hough-transform-based morphological analysis to identify board features. The perspective transformation then extracts and rectifies the adhesive regions, and constructs an adhesive region template by detecting the standard adhesive area ratio in its corresponding adhesive region. Finally, template matching is used to detect the structural adhesives. The experimental results show that this approach can accurately detect the adhesive state of PCBs and identify the qualified/unqualified locations, providing an effective vision-based detection scheme for PCB manufacturing. The main contributions of this paper are as follows: (1) A vision-based structural adhesive detection method is proposed, and its detailed algorithm is presented. (2) The developed system includes a user-friendly visualization interface, streamlining the inspection workflow. (3) Actual experiments are performed to evaluate this study, and the results validate its effectiveness. Full article

This review provides an overview of defects in silicon carbide (SiC) with potential applications as quantum qubits. It begins with a brief introduction to quantum qubits and existing qubit platforms, outlining the essential criteria a defect must meet to function as a viable qubit. The focus then shifts to the most promising defects in SiC, notably the silicon vacancy (V_Si) and divacancy (V_C-V_Si). A key challenge in utilizing these defects for quantum applications is their precise and controllable creation. Various fabrication techniques, including irradiation, ion implantation, femtosecond laser processing, and focused ion beam methods, have been explored to create these defects. Designed as a beginner-friendly resource, this review aims to support early-career experimental researchers entering the field of SiC-related quantum qubits. Providing an introduction to defect-based qubits in SiC offers valuable insights into fabrication strategies, recent progress, and the challenges that lie ahead. Full article

The study aims to improve the efficiency of oil field development at the Kalamkas field through the implementation of new methods for analyzing hydrodynamic survey data and monitoring well conditions. It is hypothesized that the use of integrated geophysical and hydrodynamic methods will enhance forecasting accuracy, optimize field operations, and increase the hydrocarbon recovery factor. An integrated approach combining pulsed neutron logging (PNL), acoustic cementometry (AC), inflow and injectivity profile evaluation methods, and specialized software for advanced data interpretation was applied, significantly improving the accuracy of well condition analysis. The analysis enabled the identification of oil and gas saturation intervals, zones of increased water cut, and cementing defects in casing, and allowed for a quantitative assessment of reservoir permeability dynamics. Hydraulic fracturing application resulted in a 10–15% increase in permeability in certain zones, with an average oil recovery factor increase of 5%. Analysis of PNL data demonstrated the transition of oil-saturated reservoirs to water saturation during development, confirmed by geophysical and pressure build-up survey results. The study identified the primary causes of increased water cut and key factors leading to production rate decline. Proposed measures for optimizing operating modes and well grid efficiency contribute to improving existing field management practices. Full article

Coke (AC) was modified and activated with sodium hydroxide (NaOH) and potassium hydroxide (KOH) to produce AC-Na and AC-K, respectively, and applied as a persulfate (PS) activator to promote phenol (Ph) removal in water. Under the given experimental conditions, compared to AC/PS (Ph removal effect was 77.09%), the Ph removal effects were 94.46% and 88.73% for AC-K/PS and AC-Na/PS, respectively. AC-K proved to be a more effective activator than AC-Na and was used for all the subsequent experiments. When PS/phenol molar ratio was 6.26:1:00, the initial system pH was 7 and the system temperature was 25 °C; the AC-K/PS system could effectively remove Ph (98.75%) from the simulated wastewater. After that, the stability of AC-K was verified. Electron paramagnetic resonance (EPR) and quenching analysis confirmed the hydroxyl free radical (•OH) to be predominant within this system. EPR combined with X-ray photoelectron spectroscopy (XPS), Fourier-transformed infrared (FTIR) spectroscopy, and Raman spectroscopy indicated that the sulfate radical (SO₄^•−) and •OH were generated due to the defects in AC-K, thereby enhancing the PS activation potency of AC-K. Additionally, the radical quenching experiments showed that the superoxide (O₂⁻) radical is a key intermediate product promoting SO₄^•− and •OH, which aided Ph removal. Both radical (SO₄^•− and •OH) and non-radical (¹O₂) pathways were found to co-exist during the removal process. The Ph removal rate of the AC-K/PS system could still reach 29.50%, even after four repeated cycles. These results demonstrate that the unique AC-K/PS system has a potential removal effect on organic pollutants in water. Full article

Over the past several decades, viral vector-based vaccines have emerged as some of the most versatile and potent platforms in modern vaccinology. Their capacity to deliver genetic material encoding target antigens directly into host cells enables strong cellular and humoral immune responses, often superior to what traditional inactivated or subunit vaccines can achieve. This has accelerated their application to a wide array of pathogens and disease targets, from well-established threats like HIV and malaria to emerging infections such as Ebola, Zika, and SARS-CoV-2. The COVID-19 pandemic further highlighted the agility of viral vector platforms, with several adenovirus-based vaccines quickly authorized and deployed on a global scale. Despite these advances, significant challenges remain. One major hurdle is pre-existing immunity against commonly used vector backbones, which can blunt vaccine immunogenicity. Rare but serious adverse events, including vector-associated inflammatory responses and conditions like vaccine-induced immune thrombotic thrombocytopenia (VITT), have raised important safety considerations. Additionally, scaling up manufacturing, ensuring consistency in large-scale production, meeting rigorous regulatory standards, and maintaining equitable global access to these vaccines present profound logistical and ethical dilemmas. In response to these challenges, the field is evolving rapidly. Sophisticated engineering strategies, such as integrase-defective lentiviral vectors, insect-specific flaviviruses, chimeric capsids to evade neutralizing antibodies, and plug-and-play self-amplifying RNA approaches, seek to bolster safety, enhance immunogenicity, circumvent pre-existing immunity, and streamline production. Lessons learned from the COVID-19 pandemic and prior outbreaks are guiding the development of platform-based approaches designed for rapid deployment during future public health emergencies. This review provides an exhaustive, in-depth examination of the historical evolution, immunobiological principles, current platforms, manufacturing complexities, regulatory frameworks, known safety issues, and future directions for viral vector-based vaccines. Full article

Cracks and dents in concrete structures are core defects that threaten building safety, but the existing YOLO series algorithms face a huge bottleneck in complex engineering scenarios. Tiny cracks are susceptible to background texture interference, leading to misjudgment. The traditional detection frame has difficulty in accurately characterizing the dent geometry, which affects the quantitative damage assessment. In this paper, we propose a Multi-level Defect Fusion Segmentation Network (MDFNet) to break through the single-task limitation through the detection segmentation synergy framework. We improve the anchor frame strategy of YOLOv11 and enhance the recall of small targets by combining Copy–Pasting, and then enhance the pixel-level characterization of crack edges and dent contours by embedding the Head Attention-Expanded Convolutional Fusion Module (HAEConv) in U-Net with squeeze-and-excitation (SE) channel attention. Joint detection loss and segmentation loss are used for task co-optimization. On our self-constructed concrete defect dataset, MDFNet significantly outperforms the baseline model. In terms of accuracy, the MDFNet Dice coefficient is 92.4%, an improvement of 4.1 percentage points compared to YOLOv11-Seg. Our mean Intersection over Union (mIoU) reaches 81.6%, with strong generalization ability under complex background interference. In terms of engineering efficacy, the model achieves a processing speed of 45 frames per second (FPS) for 640 × 640 images, which is able to meet real-time monitoring requirements. The experimental results verify the feasibility of the model in the research field of crack and dent detection in concrete structures. Full article

Space-borne gravitational wave detection missions demand ultra-precise inertial sensors with acceleration noise below $3\times {10}^{-15} \mathrm{m}/{\mathrm{s}}^2/\sqrt{\mathrm{H}\mathrm{z}}$. Patch field effects, arising from surface contaminants and nonuniform distribution of potential on the test mass (TM) and housing surfaces, pose critical challenges to sensor performance. Existing studies predominantly focus on nonuniform potential distributions while neglecting bulge effects (surface deformation caused by the adhesion of pollutants or oxides, production and processing defects, and other factors) and rely on commercial software with limited flexibility for customized simulations. This paper presents a novel boundary element partitioning and octree-based simulation algorithm to address these limitations, enabling efficient simulation of both electrostatic and geometric impacts of patch fields with low spatiotemporal complexity ($O\left(n\right)$). Leveraging this framework, we systematically investigate the influence of single patches on the TM electrostatic force ($\Delta F_x$) and stiffness ($\Delta K_{xx}$) through parametric studies. Key findings reveal that $\Delta F_x$ and $\Delta K_{xx}$ exhibit linear dependence on patch potential variation ($\Delta u$) and can be fitted by a quartic polynomial (which can be simplified in some cases, such as only a cubic term) about patch radius ($rₘ$). The proposed method’s capability to concurrently model geometric bulges and potential nonuniformity offers significant advantages over conventional approaches, providing critical insights for gravitational wave data analysis. These results establish a foundation for optimizing mitigation strategies against patch-induced noise in future space missions. Full article

The first web space of the hand plays a fundamental role in daily hand function, facilitating crucial movements, such as pinching, grasping, and opposition. The structural anomalies of acquired defects of this anatomical region, whether secondary to trauma, burns, or post-oncological surgical resections, necessitate meticulous reconstructive strategies to ensure both functional restoration and aesthetic integrity. Given the complexity and variability of first web defects, a broad spectrum of reconstructive techniques has been developed, ranging from skin grafting and local flap reconstructions to advanced microsurgical approaches. This review comprehensively examines the existing literature on first web reconstruction techniques, analyzing their indications, advantages, and limitations. Additionally, it explores innovative techniques and emerging trends in the field, such as tissue engineering, regenerative medicine, and composite tissue allotransplantation, which may revolutionize future reconstructive strategies. The primary objective is to provide clinicians with an evidence-based guide to selecting the most appropriate reconstructive strategy tailored to individual patient needs. Furthermore, we incorporate our institutional experience in managing first web defects, highlighting key surgical principles, patient outcomes, and challenges encountered. Through this analysis, we aim to refine the understanding of first web reconstruction and contribute to the ongoing evolution of hand surgery techniques. Full article

Titanium and its alloys have been widely used in high-end fields such as aerospace and biomedical engineering due to their excellent corrosion resistance and comprehensive mechanical properties. However, traditional titanium alloy processing technologies suffer from low material utilization and numerous defects. The emergence of near-net shape forming technology for powder titanium alloys via hot isostatic pressing (HIP) has broken through the limitations of traditional casting and forging, significantly improving the mechanical properties of titanium alloy materials, increasing material utilization, and shortening the production cycle of products. The application of numerical simulation technology has provided a scientific basis for the design of capsules and cores of complex high-performance components and has offered theoretical support for the densification of powders under thermomechanical coupling, becoming an essential foundation for achieving controllable shape and properties of components. This paper introduces the characteristics and process flow of HIP technology for powder titanium alloys, summarizes the current development status and research achievements of this technology both domestically and internationally, elaborates on the research progress of numerical simulation of HIP, and concludes with an analysis of the existing technological challenges and possible solutions, as well as an outlook on future development directions. Full article

Printed circuit boards (PCBs) are an indispensable part of electronic products, and their quality is crucial to the operational integrity and functional reliability of these products. Currently, existing PCB defect detection models are beset with issues such as excessive model size and parameter complexity, rendering them ill-equipped to meet the requirements for lightweight deployment on mobile devices. To address this challenge, this paper proposes a lightweight detection model, GESC-YOLO, developed through modifications to the YOLOv8n architecture. First, a new lightweight module, C2f-GE, is designed to replace the C2f module of the backbone network, which effectively reduces the computational parameters, and at the same time increases the number of channels of the feature map to enhance the feature extraction capability of the model. Second, the neck network employs the lightweight hybrid convolution GSConv. By integrating it with the VoV-GSCSP module, the Slim-neck structure is constructed. This approach not only guarantees detection precision but also enables model lightweighting and a reduction in the number of parameters. Finally, the coordinate attention is introduced into the neck network to decompose the channel attention and aggregate the features, which can effectively retain the spatial information and thus improve the detection and localization accuracy of tiny defects (defect area less than 1% of total image area) in PCB defect images. Experimental results demonstrate that, in contrast to the original YOLOv8n model, the GESC-YOLO algorithm boosts the mean Average Precision (mAP) of PCB surface defects by 0.4%, reaching 99%. Simultaneously, the model size is reduced by 25.4%, the parameter count is cut down by 28.6%, and the computational resource consumption is reduced by 26.8%. This successfully achieves the harmonization of detection precision and model lightweighting. Full article

To address the challenges in existing multi-sensor data fusion methods for rail surface defect diagnosis, particularly their limitations in fully exploiting potential synergistic information among multimodal data and effectively bridging the semantic gap between heterogeneous multi-source data, this paper proposes a diagnostic approach based on a Progressive Joint Representation Graph Attention Fusion Network (PJR-GAFN). The methodology comprises five principal phases: Firstly, shared and specific autoencoders are used to extract joint representations of multimodal features through shared and modality-specific representations. Secondly, a squeeze-and-excitation module is implemented to amplify defect-related features while suppressing non-essential characteristics. Thirdly, a progressive fusion module is introduced to comprehensively utilize cross-modal synergistic information during feature extraction. Fourthly, a source domain classifier and domain discriminator are employed to capture modality-invariant features across different modalities. Finally, the spatial attention aggregation properties of graph attention networks are leveraged to fuse multimodal features, thereby fully exploiting contextual semantic information. Experimental results on real-world rail surface defect datasets from domestic railway lines demonstrate that the proposed method achieves 95% diagnostic accuracy, confirming its effectiveness in rail surface defect detection. Full article

Few-shot steel surface defect generation remains challenging due to the limited availability of training samples and the complex visual characteristics of industrial defects. Traditional data augmentation techniques often fail to capture the diverse manifestations of defects, resulting in suboptimal detection performance. While existing stable diffusion models possess robust generative capabilities, they encounter domain knowledge transfer limitations when applied to industrial settings. To address these constraints, stable industrial defect generation (stableIDG) is proposed, which enhances stable diffusion through three key improvements: (1) fine-tuning with low-rank embedding adaptation to accommodate steel surface defect generation; (2) implementation of personalized identifiers associated with defect regions to prevent malformation during generation; and (3) development of a mask-guided defect learning mechanism that directs network attention toward critical defect regions, thereby enhancing the fidelity of generated details. The method was validated on a newly constructed Medium and Heavy Plate Surface Defect Dataset (MHPSD) from a real industrial environment. Experiments demonstrated that stableIDG achieved better image generation quality compared to existing methods, such as textual inversion. Furthermore, when the generated samples were used for data augmentation, the detection network performance was effectively enhanced. Detection recall significantly improved for both defect classes, increasing from 0.656 to 0.908 for Inclusion defects and from 0.86 to 0.986 for Foreign Object Embedding defects. These results demonstrate the effectiveness of the proposed method for industrial defect detection in data-scarce scenarios. Full article