Browse Topic: Sensors and actuators
This paper details the successful scaling demonstration of a comprehensive supply chain screening process for commercial off-the-shelf (COTS) motherboard subassemblies used in tactical servers for naval applications. Our approach leverages Power Fingerprinting (PFP) technology, which uses unintended analog emissions and machine learning to provide independent, non-destructive, and scalable integrity assessment of microelectronics. The primary goal of the effort was to demonstrate the effectiveness and scalability of the PFP screening process without disrupting or delaying the manufacturing workflow. The screening successfully detected hardware and firmware modifications and identified two cases of abnormal behavior: unusual BIOS power reset and elevated CPU sensor readings on two motherboard subassemblies. Following our quality control forensic analysis, we determined the root cause of these anomalies and their potential impact on the host platform.
Modern defense manufacturing and sustainment require timely engineering decisions (e.g., inspection triage, rework/accept decisions, and process adjustment) based on the as-built geometric quality. Advanced machining systems generate rich multichannel controller and sensor streams, yet accurate geometric deviation labels remain costly and delayed because they depend on downstream metrology. This paper introduces ChronosGD, a retrieval-based virtual metrology framework. ChronosGD predicts pointwise geometric deviation from multichannel time series data by: (1) retrieving the most similar historical process windows in a frozen Chronos-2 embedding space, and (2) transferring deviation information through similarity-weighted aggregation. ChronosGD avoids plant-specific gradient retraining during deployment; adaptation is achieved by refreshing a labeled historical memory as new inspected parts become available, while preserving traceability through explicit neighbor provenance.
Biomanufacturing uses microorganisms to produce chemicals or materials of interest, much like a brewery uses fermentation by yeast to produce the alcohol in beer. Biomanufacturing relies upon synthetic biology to reprogram yeast or other microorganisms to produce something of greater value, such as fuel, food, or pharmaceuticals. Industrial biomanufacturing has made significant advances and the products it can deliver include reactive coatings and textiles, sensors, optical materials that can bend light, and new therapeutics such as antimicrobials and vaccines. The convergence of synthetic biology, robotics, and artificial intelligence is opening the way to produce materials never before possible in the commercial market. These same technologies create the opportunity for the miniaturization of this technology to fit into ever more compact spaces, bringing forward deployment of these mini-factories closer and closer to the point of need.
This paper details the development of an intelligence and inspection platform consisting of an attritable sub-250g UAV, a ground control station, and a visualization interface for users. The UAV architecture combines onboard obstacle detection and avoidance along with simultaneous localization and mapping to have full autonomous navigation inside of complicated GPS-denied environments. The ROS 2-to-Unreal Engine data pipeline allows for sensor fusion, data cleansing, and initial analysis as well as creation of a high-fidelity real-time 3D digital twin. The visualization interface allows users to easily identify critical features and turn data into intelligence to support decision making by soldiers and first responders.
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This research is meant to enhance the analytic capabilities of OneSAF for usage as a Monte Carlo-style data generator for use in large problem space trade studies. Using novel ground vehicle data such as: RHA armor values, weapon penetration prediction models, and sensor values, new models can be developed in OneSAF for use in data generation and analysis. This process and companion software developed for this purpose enables the rapid construction and evaluation of differing vehicle variants in a fraction of the time of the baseline process, improving the efficiency of using OneSAF as a data analysis tool. This approach facilitates a more comprehensive virtual experimentation approach that can use manufactured data as a part of the process.
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The Electro-Mechanical Brake (EMB) system is a dry-type Brake-by-Wire technology that eliminates hydraulic components and directly controls friction braking using electrical actuators at each wheel. The EMB architecture consists of a Main Center Control Unit, a redundant Backup Center Control Unit, and four Wheel Control Units communicating via CAN FD. Due to its direct involvement in vehicle braking, compliance with ISO 26262 functional safety requirements is critical. As system complexity increases, potential risks such as hardware failures and communication faults must be systematically addressed. The proposed TSC was developed according to ISO 26262, covering the concept phase (Part 3), system-level development (Part 4), and software implementation (Part 6). Safety goals and Functional Safety Requirements derived from HARA are used to guide system architecture design and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe operation. Verification is conducted at both system and vehicle levels using ECU-in-the-Loop Simulation (EILS), Hardware-in-the-Loop Simulation (HILS), and real-vehicle tests. Fault scenarios, including Main Center Control Unit failures and CAN communication losses, are injected using a custom LabVIEW-based fault injection tool. The study evaluates Fault Tolerant Time Interval (FTTI) settings, error handling mechanisms, and control handover strategies under fault conditions. The results show that redundancy and localized communication enable stable operation and smooth control transfer within the FTTI window without noticeable impact on braking performance or driver awareness. This study demonstrates the robustness of the proposed EMB architecture. Future work will focus on prognostics and maintenance strategies to support safe deployment in autonomous and electric vehicles. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
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.
For decades, hydraulic systems have been relied upon to do all the heavy lifting in aerospace. They are powerful, reliable, and deeply embedded in how aircraft are designed, to the extent that - for many engineers - they are simply part of the landscape. Now, however, things are beginning to change. From advanced air mobility platforms now entering certification to next-generation commercial aircraft on 10-year horizons, electric and electro-hydraulic actuation is steadily replacing the heavy, centralized hydraulic architectures that have defined flight control for decades. Understanding why means stepping back from the actuator itself and looking at the aircraft as a whole system - and, increasingly, as an integrated motion control challenge.
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 malfunction of the aircraft windshield electric heating system, particularly arc discharge, poses a serious threat to flight safety by causing glass breakage. A systematic study was conducted on the causes and effects of arc faults on windshield structural integrity, employing macroscopic observation, microscopic analysis, and energy dispersive spectroscopy (EDS) following a windshield fracture incident. The results indicate that arc discharge typically occurs at the interface between the heating film busbar and adjacent structures. Localized high temperatures cause the outer glass to fracture, generating radial cracks. The ablation of the busbar silver coating and the carbonization of the PVB interlayer are direct evidence of arc action, whereas the heating wire remains a passive component affected by the high-temperature environment. The fault is primarily attributed to local disbonding at the busbar interface and moisture ingress. Based on the findings, recommendations are proposed for process optimization and inspection method improvement, providing a basis for the safe design and maintenance of windshield structures.
With the development of controlled nuclear fusion technology, the tokamak device, as the most promising magnetic confinement fusion reactor for advanced engineering applications, requires remote maintenance of its internal components, which has become a key factor affecting both operational efficiency and safety. As a critical component directly exposed to high-temperature plasma, the divertor target plate needs to be periodically replaced and carefully maintained to ensure stable and reliable reactor operation. However, this region is subject to extreme conditions, including high temperature, high vacuum, and intense radiation, making conventional manual maintenance infeasible. This necessitates the development of intelligent and automated teleoperation systems. To address the automated assembly and disassembly requirements of divertor target plates, this study designs an integrated target plate actuator comprising key functional units: a positioning module, a screwing module, a quick-change module, and a passive compliance structure. The actuator achieves rapid and precise alignment with target plate holes, accommodates bolts of different specifications, and exhibits excellent impact resistance. Furthermore, stiffness and mechanical analyses, supported by finite element simulations, verify the actuator’s safety and reliability under high loads and impact forces. To further enhance operational performance, a segmented disassembly and assembly control strategy based on reinforcement learning is proposed, enabling the actuator to adaptively handle torque variations and ensure precise and stable bolt operations. The results demonstrate that the proposed actuator and control strategy significantly improve the accuracy, stability, and efficiency of target plate operations under complex working conditions, providing a reliable solution for automated divertor maintenance in tokamak devices.
When quadrotor unmanned aerial vehicles (UAVs) operate in urban low-altitude airspace, especially within complex environments, their sensor perception signals are highly susceptible to blockages, deviations, and the inclusion of high-frequency noise. These factors, in turn, induce nonlinear variations in the UAVs’ flight mechanical properties, giving rise to abnormal flight stability issues such as attitude jitter, altitude fluctuations, and trajectory deviations. To address these challenges, this paper puts forward a method aimed at enhancing the positional accuracy of quadrotor UAVs, which is based on Extended Kalman Filter (EKF) multi-sensor fusion. In conjunction with the redundant configuration of sensors, a proportional-integral controller is specifically designed to allow optical flow sensors to compensate for the speed data generated by inertial sensors. Building on the EKF method, a comprehensive data fusion model is established, encompassing both position and speed states. Leveraging the MATLAB platform, trajectory flight simulations are conducted, utilizing multi-sensor data fused via EKF, with the sensor suite including GPS, IMU, Optical Flow sensors, and Barometers. The simulation results demonstrate that this proposed method can effectively mitigate the adverse impacts of environmental interference and sensor noise on the positional accuracy of quadrotors. By continuously correcting position information and accurately estimating position states, it significantly improves the UAVs’ flight position accuracy. This research outcome lays a robust and theoretically sound foundation for in-depth investigations on critical issues related to general aviation applications, such as the safe and efficient autonomous flight, adaptive and reliable intelligent navigation, and ultra-precise and mission-critical operations of quadrotor UAVs, thereby significantly contributing to the sustained and innovative advancement of the field.
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