Browse Topic: Electrical, Electronics, and Avionics
This study proposes an intelligent automotive roof frame design method based on the middle layer and component technology on CATIA. It aims to solve core roof modeling issues: determining geometric input quantity but uncertain attributes (tangent vectors, normal vectors, number of curve segments, number of surface patches, and boundaries), high manual interaction dependence, and poor knowledge reuse, to realize efficient design knowledge reuse. Methodologically, it builds a feature-driven parametric template, develops a knowledge rule-embedded componentized UDF library (reducing repeated modeling and geometric reconstruction needs), and integrates knowledge engineering for geometric input verification and operation direction control, eliminating curve/surface attribute uncertainty impacts. Verification shows the template stably generates roof crossbeams under simple/complex inputs, improving model robustness and reuse rate, reducing design workload, shortening verification cycles, and providing an extensible solution for white body design.
g-C₃N₄, a metal-free semiconductor photocatalyst, demonstrates remarkable potential, but its practical application in pollutant degradation is significantly limited by the rapid recombination of photogenerated electron-hole pairs and low photocatalytic efficiency. To address this, a series of magnetic recyclable g-C₃N₄/CoFe₂O₄ composite photocatalysts with different CoFe₂O₄ doping ratios were innovatively designed and prepared via thermal polymerization, sol- gel, and combined with ultrasonic and heat treatment processes. The novelty of this composite design lies in the effective integration of magnetic CoFe₂O₄ with g-C₃N₄ through a heterojunction structure. It substantially boosts the absorption of visible light. Concurrently, it effectively fosters the separation and mobility of photo-induced charge carriers. The composite materials were systematically characterized by X-ray diffraction, thermogravimetric analysis, scanning electron microscopy with energy-dispersive X-ray spectroscopy, photoluminescence spectroscopy, and ultraviolet-visible diffuse reflectance spectroscopy. Using tetracycline hydrochloride as the target pollutant, the photocatalytic activity of the composites was evaluated under visible light irradiation, and the effects of initial concentration, catalyst dosage, and the influence of solution pH on degradation efficiency were also examined. The results indicated that the composite with a CoFe₂O₄ to g-C₃N₄ mass ratio of 1:3 (denoted as 3-CN/CFO) exhibited the optimal performance: a TCH degradation rate of 80.29 % within 105 minutes and a total organic carbon removal rate of 61.63 %. After five consecutive cycling experiments, the degradation efficiency remained above 70 %, demonstrating good reusability and stability. The performance improvement is attributed to the formation of heterojunctions in the composite, which effectively facilitates charge separation, inhibits carrier recombination, and enhances visible light absorption. Furthermore, the inherent magnetism of the composite permits efficient recovery, streamlining its integration into practical applications. Toward the purification of antibiotic-contaminated water, this research proposes a viable method for fabricating highly effective and recyclable photocatalysts.
Conventional measurement instruments such as scales, thermocouples, and laser-based technologies present challenges when used on lengthy and winding underground pipelines. These methods are often not feasible because of physical constraints, the challenge of light traveling in curves, and the need for large, energy-intensive sensors. Ultrasonic and microwave techniques both face challenges in making long-distance measurements because of rapid signal weakening and high energy requirements, which make them impractical for small pipes. This study introduces an original technique for Time-of-Flight (ToF) estimation using the Discrete Logarithmic Frequency (DLF) method to address these limitations. By analyzing the time–frequency correlations of signals transmitted through channels, the proposed technique enhances the precision and dependability of ToF measurements. By employing the DLF method, we are able to effectively gather and assess the signal’s performance as conduit lengths vary.
A research team led by Professor Lin Gui at the Institute of Physics and Chemistry, Chinese Academy of Sciences, reports the first fabrication of multi-layer flexible batteries using a combination of liquid metal microfluidic perfusion and plasma-based reversible bonding techniques.
A machine-learning strategy has generated a new class of ultra-high strength and ductility steel for 3D printing that costs less, resists rust, and requires only a fraction of the usual processing time.
Penn Engineers have developed a novel design for solar-powered data centers that will orbit the Earth and could realistically scale to meet the growing demand for AI computing while reducing the environmental impact of data centers.
Aircraft assembly systems, as a critical phase in aerospace manufacturing, face significant challenges in maintaining production efficiency and ensuring product quality. This complex manufacturing system exhibits two distinct characteristics: (1) tightly coupled interactions among manufacturing elements involving process sequences, material flows, and equipment utilization; and (2) dynamic resource allocation and material distribution plans. The inherent variability in production element configurations often leads to operational instability and schedule deviations, which may result in abnormal production states. To address these challenges, this study proposes a data-driven predictive framework that integrates Long Short-Term Memory (LSTM) neural networks with multi-criteria evaluation. The developed LSTM-based model effectively forecasts two critical production indicators of cycle time and balance rate, achieving temporal prediction through historical operational data analysis. The proposed methodology facilitates timely anomaly detection and early warning, allowing proactive risk mitigation and ensuring sustained production system stability. This research contributes to advancing intelligent monitoring and control strategies for aircraft assembly operations within data-driven manufacturing environments.
Metallurgical cranes have a high risk of structural fatigue damage and failure under complex working conditions such as high temperature, heavy load, and strong electromagnetic interference. This article proposes a data-driven structural fatigue damage health monitoring system. This system integrates fiber Bragg grating sensing technology, rigid flexible coupling multi-body dynamics simulation, and big data analysis methods to construct a sensor optimization layout strategy based on rigid flexible coupling virtual prototype simulation, achieving real-time perception of stress states in key parts such as the mid span and end beam corners of the main beam. Develop a visualization system that integrates health monitoring, damage diagnosis, and life prediction. This system can dynamically evaluate the structural health status of metallurgical cranes and predict the remaining life of the structure based on a nonlinear cumulative damage model. On site engineering applications have shown that the monitoring and prediction visualization system can effectively improve the intelligent and safe operation and maintenance level of metallurgical cranes, providing a data foundation and possibility for their predictive maintenance.
Conveyor belt fault detection is critical for ensuring the safety and efficiency of industrial material transportation. In this study, a screen-printed flexible strain sensor based on a thermoplastic polyurethane (TPU) substrate and graphene conductive ink was fabricated. The sensor exhibited excellent flexibility, mechanical robustness, and stable electromechanical performance. Comprehensive evaluations were conducted, including microstructural analysis, strain sensitivity, hysteresis, dynamic response, and long-term cycling stability. The results demonstrated that a two-layer graphene configuration achieved an optimal balance between sensitivity and structural stability, showing high gauge factor, fast response, and reliable cyclic performance. Furthermore, the sensor was applied to conveyor belt fault monitoring. Experiments validated its ability to detect both halting faults and foreign object intrusions, with distinctive resistance signal features enabling not only fault occurrence detection but also identification of fault location, type, and severity. These findings highlight the potential of the proposed flexible sensor system as a promising solution for intelligent conveyor belt monitoring in harsh industrial environments.
The inconsistency in bearing data distributions under diverse conditions often affects the representations of the faulty data and leads to indistinct decision boundaries and even negative transfer resulted from overlapping class distributions, greatly limiting the accuracy of the diagnosis model. To cope with the challenge, a pseudo-label-guided dual-supervised alignment (PDSA) method is developed for bearing fault diagnosis across diverse operating scenarios in this paper. To address the fixed alignment strategy issue, an adaptive distribution alignment layer is incorporated to ResNet18 to achieve dynamic data distribution alignment under varying condition, To enhance classification performances, a dual-supervised mechanism, comprising shallow-layer supervised contrastive learning is introduced through target domain pseudo-labels in target domain and deep-layer regularization class consistency. Experiments on two publicly available bearing datasets demonstrated this model realizes refined class-level alignment, strengthens fault states representation, and shows notable superiority in both accuracy and robustness.
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