Search Results (2,368)

Highly integrable silicon carbide (SiC) has emerged as a promising platform for photonic integrated circuits (PICs), offering a comprehensive set of material and optical properties that are ideal for the integration of nonlinear devices and solid-state quantum defects. However, despite significant progress in nanofabrication technology, the development of SiC on an insulator (SiCOI)-based photonics faces challenges due to fabrication-induced material optical losses and complex processing steps. An alternative approach to mitigate these fabrication challenges is the direct deposition of amorphous SiC on an insulator (a-SiCOI). However, there is a lack of systematic studies aimed at producing high optical quality a-SiC thin films, and correspondingly, on evaluating and determining their optical properties in the telecom range. To this end, we have studied a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C₃H₁₂Si₃), and chemical vapor deposition (CVD) processes for the deposition of SiC thin films in a low-temperature range (650–800 °C) on a multitude of different substrates. We have successfully demonstrated the fabrication of smooth, uniform, and stoichiometric a-SiCOI thin films of 20 nm to 600 nm with a highly controlled growth rate of ~0.5 Å/s and minimal surface roughness of ~5 Å. Spectroscopic ellipsometry and resonant micro-photoluminescence excitation spectroscopy and mapping reveal a high index of refraction (~2.7) and a minimal absorption coefficient (<200 cm⁻¹) in the telecom C-band, demonstrating the high optical quality of the films. These findings establish a strong foundation for scalable production of high-quality a-SiCOI thin films, enabling their application in advanced chip-scale telecom PIC technologies. Full article

The corrosion of high-strength steel wires is a key factor impacting the durability and reliability of cable-stayed bridges. In this study, the corrosion pit features on a high-strength steel wire, which had been in service for 27 years, were extracted and modeled using three-dimensional point cloud data obtained through 3D surface scanning. The Otsu method was applied for image binarization, and each corrosion pit was geometrically represented as an ellipse. Key pit parameters—including length, width, depth, aspect ratio, and a defect parameter—were statistically analyzed. Results of the Kolmogorov–Smirnov (K–S) test at a 95% confidence level indicated that the directional angle component (θ) did not conform to any known probability distribution. In contrast, the pit width (b) and defect parameter (Φ) followed a generalized extreme value distribution, the aspect ratio (b/a) matched a Beta distribution, and both the pit length (a) and depth (d) were best described by a Gaussian mixture model. The obtained results provide valuable reference for assessing the stress state, in-service performance, and predicted remaining service life of operational stay cables. Full article

Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations in process parameters and material behavior during fabrication. In-situ monitoring and advanced process control systems have been increasingly integrated into MEAM to address these issues, enabling real-time detection of defects, optimization of printing conditions, reliability of fabricated parts, and enhanced control over mechanical properties. This review examines the state-of-the-art in-situ monitoring techniques, including thermal imaging, vibrational sensing, rheological monitoring, printhead positioning, acoustic sensing, image recognition, and optical scanning, and their integration with process control strategies, such as closed-loop feedback systems and machine learning algorithms. Key challenges, including sensor accuracy, data processing complexity, and scalability, are discussed alongside recent advancements and their implications for industrial applications. By synthesizing current research, this work highlights the critical role of in-situ monitoring and process control in advancing the reliability and precision of MEAM, paving the way for its broader adoption in high-performance manufacturing. Full article

Efficient maintenance is paramount for rail transport systems to avoid catastrophic accidents. Therefore, a method that enables the early detection of defects in critical components is crucial for increasing the availability of rolling stock and reducing maintenance costs. This work’s main contribution is the proposal of a methodology for analyzing vibration signals. The vibration signals, obtained from a bogie axle on a test bench, are decomposed into intrinsic functions, to which classical signal processing techniques are then applied. Finally, decision trees are employed to characterize the axle’s state, yielding excellent results. Full article

Pile foundations are critical structural elements, and their integrity is essential for ensuring the stability and safety of construction projects. Low-strain pile integrity testing (LSPIT) is widely used for defect detection; however, conventional manual interpretation of reflectograms is both time-consuming and susceptible to human error. This study presents a deep learning-driven approach utilizing transfer learning with convolutional neural networks (CNNs) to automate pile defect detection. A dataset of 328 reflectograms collected from real construction sites, including 246 intact and 82 defective samples, was used to train and evaluate the model. To address class imbalance, oversampling techniques were applied. Several state-of-the-art pretrained CNN architectures were compared, with ConvNeXtLarge achieving the highest accuracy of 98.2%. The accuracy reported was achieved on a dedicated test set using real reflectogram data from actual construction sites, distinguishing this study from prior work relying primarily on synthetic data. The proposed novelty includes adapting pre-trained CNN architectures specifically for real-world pile integrity testing, addressing practical challenges such as data imbalance and limited dataset size through targeted oversampling techniques. The proposed approach demonstrates significant improvements in accuracy and efficiency compared to manual interpretation methods, making it a promising solution for practical applications in the construction industry. The proposed method demonstrates potential for generalization across varying pile lengths and geological conditions, though further validation with broader datasets is recommended. Full article

Electrons trapped above the surface of helium provide a means to study many-body physics free from the randomness that comes from defects in other condensed-matter systems. Localizing an electron in an electrostatic quantum dot makes its energy spectrum discrete, with controlled level spacing. The lowest two states can act as charge qubit states. In this paper, we study how the coupling to the quantum field of capillary waves on helium—known as ripplons—affects electron dynamics. As we show, the coupling can be strong. This bounds the parameter range where electron-based charge qubits can be implemented. The constraint is different from the conventional relaxation time constraint. The electron–ripplon system in a dot is similar to a color center formed by an electron defect coupled to phonons in a solid. In contrast to solids, the coupling in the electron on helium system can be varied from strong to weak. This enables a qualitatively new approach to studying color center physics. We analyze the spectroscopy of the pertinent synthetic color centers in a broad range of the coupling strength. Full article

Pore defects are one of the most common defects in the aluminum alloy electron beam welding process. In this paper, research on the pore defects and related mechanisms of the electron beam welding of dissimilar aluminum alloys was carried out with 1060 and 2A12 aluminum alloys. Under the test conditions, the pore defects of the aluminum alloy joint were related to the penetration status, the porosity of the critically penetrated joint was low, and the porosity of the beam joint increased when it was slightly penetrated. When the welding speed changed from 300 mm/min to 1200 mm/min, the porosity in the critically penetrated joint first increased and then decreased. When the welding speed was set to 300 mm/min and the beam current was set to 26 mA, the porosity of the joints reached its minimum value at 0.23%. Based on the actual process of electron beam welding, a flow simulation model was established to study the aluminum alloy welding process. The stability of the keyhole was related to the electron beam energy density reaching the inner keyhole, so increasing the electron beam for the fully penetrated joints was advantageous for reducing the pore defects. Full article

Selective laser melting (SLM) offers a novel approach for manufacturing intricate structures, broadening the application of titanium alloy parts in the aerospace industry. After the build period, heat treatments of annealing (AT) and hot isostatic pressing (HIP) are often implemented, but a comparison of their mechanical performances based on the specimen orientation is still lacking. In this study, horizontally and vertically built Ti6Al4V SLM specimens that underwent the aforementioned treatments, together with their microstructural and defect characteristics, were, respectively, investigated using metallography and X-ray imaging. The mechanical properties and failure mechanism, via fracture analysis, were obtained. The critical factors influencing the mechanical properties and the correlation of the fatigue lives and failure origins were also estimated. The results demonstrate that the mechanical performances were determined by the α-phase morphology and defects, which included micropores and fewer large lack-of-fusion defects. Following the coarsening of the α phase, the strength decreased while the plasticity remained stable. With the discrepancy in the defect occurrence, anisotropy and scatter of the mechanical performances were introduced, which was significantly alleviated with HIP treatment. The fatigue failure origins were governed by defects and the α colony, which was composed of parallel α phases. Approximately linear relationships correlating fatigue lives with the X-parameter and maximum stress amplitude were, respectively, established in the AT and HIP states. The results provide an understanding of the technological significance of the evaluation of mechanical properties. Full article

Substation equipment defect detection is a critical aspect of ensuring the reliability and stability of modern power grids. However, existing deep-learning-based detection methods often face significant challenges in real-world deployment, primarily due to low detection accuracy and inconsistent anomaly definitions across different substation environments. To address these limitations, this paper proposes the Language-Guided Enhanced Anomaly Power Equipment Detection Network (LEAD-Net), a novel framework that leverages text-guided learning during training to significantly improve defect detection performance. Unlike traditional methods, LEAD-Net integrates textual descriptions of defects, such as historical maintenance records or inspection reports, as auxiliary guidance during training. A key innovation is the Language-Guided Anomaly Feature Enhancement Module (LAFEM), which refines channel attention using these text features. Crucially, LEAD-Net operates solely on image data during inference, ensuring practical applicability. Experiments on a real-world substation dataset, comprising 8307 image–text pairs and encompassing a diverse range of defect categories encountered in operational substation environments, demonstrate that LEAD-Net significantly outperforms state-of-the-art object detection methods (Faster R-CNN, YOLOv9, DETR, and Deformable DETR), achieving a mean Average Precision (mAP) of 79.51%. Ablation studies confirm the contributions of both LAFEM and the training-time text guidance. The results highlight the effectiveness and novelty of using training-time defect descriptions to enhance visual anomaly detection without requiring text input at inference. Full article

This paper takes a look at training a generator model based on a limited dataset that can fit the distribution of the original dataset, improving the reconstruction ability of wafer datasets. High-fidelity wafer defect image generation remains challenging due to limited real data and poor physical authenticity of existing methods. We propose an enhanced StyleGANv3 framework with two key innovations: (1) a Heterogeneous Kernel Fusion Unit (HKFU) enabling multi-scale defect feature refinement via spatiotemporal attention and dynamic gating; (2) a Dynamic Adaptive Attention Module (DAAM) adaptively boosting discriminator sensitivity. Experiments on Mixtype-WM38 and MIR-WM811K datasets demonstrate state-of-the-art performance, achieving FID scores of 25.20 and 28.70 alongside SDS values of 36.00 and 35.45. The proposed method in this article helps alleviate the problem of limited datasets and makes an important contribution to data preparation for downstream classification and detection tasks. Full article

Currently, the shear-extrusion behavior of solid propellants (SPs), which comprise a significant volume fraction of micro-/nanoscale solid particles (e.g., octogen/HMX), nitroglycerin as a plasticizer/solvent, nitrocellulose as a binder, and other functional additives, is still insufficiently understood. While the rheology of highly filled polymers has been extensively documented, the rheological behavior of SPs within the practical processing temperature range of 80–95 °C remains poorly understood. This study investigated, in particular, the pressure dependence of the viscosity of SPs melts during steady-state shear flow. Steady-state shear measurements were conducted using a twin-bore capillary rheometer with capillary dies of varying diameters and lengths to explore the viscosity dependence of SPs. The results reveal that interface defects between octogen particles and the polymer matrix generate a melt pressure range of 3–30 MPa in the long capillary die, underscoring the non-negligible impact of pressure on the measured viscosity (η). At constant temperature and shear rate, the measured viscosity of SPs exhibits strong pressure dependence, showing notable deviations in pressure sensitivity (β), which was found to be greatly relevant to the contents of solvent and solid particles. Such discrepancies are attributed to the compressibility of particle–particle and particle–polymer networks during capillary flow. The findings emphasize the critical role of pressure effect on the rheological properties of SPs, which is essential for optimizing manufacturing processes and ensuring consistent propellant performance. Full article

Lead sulfide colloidal quantum dots (PbS QDs) have demonstrated great potential in short-wave infrared (SWIR) photodetectors due to their tunable bandgap, low cost, and broad spectral response. While significant progress has been made in surface ligand modification and defect state passivation, studies focusing on the interface between QDs and electrodes remain limited, which hinders further improvement in device performance. In this work, we propose an interface engineering strategy based on 3-aminopropyltriethoxysilane (APTES) to enhance the interfacial contact between PbS QD films and ITO interdigitated electrodes, thereby significantly boosting the overall performance of SWIR photodetectors. Experimental results demonstrate that the optimal 0.5 h APTES treatment duration significantly enhances responsivity by achieving balanced interface passivation and charge carrier transport. Moreover, The APTES-modified device exhibits a controllable dark current and faster photo-response under 1310 nm illumination. This interface engineering approach provides an effective pathway for the development of high-performance PbS QD-based SWIR photodetectors, with promising applications in infrared imaging, spectroscopy, and optical communication. Full article

We provide an approach to detect a nitrogen-vacancy (NV) center, which might be a defect in a diamond nanomembrane, using a dual-coupling optomechanical system. The NV center modifies the energy-level structure of a dual-coupling optomechanical system through dressed states arising from its interaction with the mechanical membrane. Thus, we study the photon blockade in the cavity of a dual-coupling optomechanical system in which an NV center is embedded in a single-crystal diamond nanomembrane. The NV center significantly influences the statistical properties of the cavity field. We systematically investigate how three key NV center parameters affect photon blockade: (i) its coupling strength to the mechanical membrane, (ii) transition frequency, and (iii) decay rate. We find that the NV center can shift, give rise to a new dip, and even suppress the original dip in a bare quadratic optomechanical system. In addition, we can amplify the effect of the NV center on photon statistics by adding a gravitational potential when the NV center has little effect on photon blockade. Therefore, our study provides a method to detect diamond nanomembrane defects in a dual-coupling optomechanical system. Full article

Three-dimensional (3D) concrete printing (3DCP) has gained significant attention over the last decade due to its many claimed benefits. The absence of effective real-time quality control mechanisms, however, can lead to inconsistencies in extrusion, compromising the integrity of 3D-printed structures. Although the importance of quality control in 3DCP is broadly acknowledged, research lacks systematic methods. This research investigates the feasibility of using ultrasonic pulse velocity (UPV) as a practical, in situ, real-time monitoring tool for 3DCP. Two different groups of binders were investigated: limestone calcined clay (LC³) and zeolite-based mixes in binary and ternary blends. Filaments of 200 mm were extruded every 5 min, and UPV, pocket hand vane, flow table, and viscometer tests were performed to measure pulse velocity, shear strength, relative deformation, yield stress, and plastic viscosity, respectively, in the fresh state. Once the filaments presented printing defects (e.g., filament tearing, filament width reduction), the tests were concluded, and the open time was recorded. Isothermal calorimetry tests were conducted to obtain the initial heat release and reactivity of the supplementary cementitious materials (SCMs). Results showed a strong correlation (R² = 0.93) between UPV and initial heat release, indicating that early hydration (ettringite formation) influenced UPV and determined printability across different mixes. No correlation was observed between the other tests and hydration kinetics. UPV demonstrated potential as a real-time monitoring tool, provided the mix-specific pulse velocity is established beforehand. Further research is needed to evaluate UPV performance during active printing when there is an active flow through the printer. Full article

Next to solid wood, laminated particleboard is the most widely used wood-based material in the furniture industry. Ensuring the high quality of the laminate surface after machining is of critical importance for furniture manufacturers, particularly prior to the edge banding process, as this process significantly influences the final aesthetic and functional quality of panel elements. The objective of this review article is to gather and evaluate the current state of knowledge regarding the influence of machining process parameters and the physical and mechanical properties of laminated particleboard on machining quality. Particular emphasis is placed on the occurrence of laminate damage, commonly referred to as delamination, a prevalent defect in the furniture manufacturing sector. Both categories of influencing factors—process-related and material-related—are analyzed within the context of the three primary technological processes employed in the woodworking industry, namely drilling, cutting, and milling. The analysis revealed that a persistent research gap concerns the relationship between machining quality and material parameters, particularly in the case of milling—a process of critical importance in the furniture industry. Full article