Detecting unmanned aerial vehicles (UAVs) in complex low-altitude scenes remains challenging due to limited target details and background interference. To address these challenges, this paper proposes DARA-Net, a lightweight UAV detection framework that coordinates detail preservation, efficient feature extraction, and multiscale aggregation. Built upon YOLOv11, DARA-Net integrates adaptive window reduction (AWR) to retain informative local features during downsampling, a separable aggregation block (SAB) to improve feature representation with fewer parameters, and a directional weighted pyramid (DWP) to enhance cross-scale feature fusion. Experiments on a visible-light UAV dataset containing 25,941 images demonstrate that DARA-Net achieves 97.0% precision, 91.1% recall, 95.1% mAP@0.5, and 62.0% mAP@0.5:0.95. Compared with the YOLOv11 baseline, recall improves by 2.6 percentage points, while the parameter count decreases by approximately 30.6% and model size decreases by 29.1%, reaching 1.79 M parameters and 3.9 MB, respectively. Compared with YOLOv26n, DARA-Net achieves a higher mAP@0.5:0.95 (62.0% versus 60.77%) with approximately 24.6% fewer parameters and a 28.2% smaller model file, while maintaining comparable mAP@0.5. Additional validation on an external UAV dataset further supports its competitive detection performance. These results highlight the value of DARA-Net as a compact UAV detector that prioritizes localization performance and reduced parameter and storage requirements.

Based on the theorem of die-sinking micro electrical discharge machining (EDM) with a composite material electrode (CME), this study proposed to use two high wear rate electrode foils to bond and clamp a low wear rate electrode foil, and stack them to produce a sandwich electrode that could be reused, in principle. This sandwich electrode was used to process the deep narrow groove arrays by EDM. The two sub-electrodes on the outer side rapidly wore out during processing. Therefore, the width and depth of the processed narrow groove were determined by the middle sub-electrode with a low wear rate. While processing the deep narrow groove, the discharge grinding of the sandwich electrode was adaptively completed to avoid the extremely thin low wear rate sub-electrode being exposed for too long, making it unstable or even damaged. Based on this principle, a sandwich electrode composed of Cu foil and H62 brass foil was fabricated. Taking the example of machining a narrow groove array with a spacing of 300 μm on high-speed steel workpieces, the reasonable open circuit voltage, pulse frequency, and Cu foil sub-electrode thickness for EDM were studied. Based on this, deep narrow groove arrays with depths exceeding 2.1 mm and aspect ratio greater than 13 were machined. A self-sharpening model of the sandwich electrode was established based on the die-sinking micro EDM theorem with CME. Through simulation iteration, the process of self-sharpening and rapid convergence of narrow groove top cup shape into microstructures was studied.

Micro-electrochemical machining (micro-ECM) is a non-contact subtractive process in which material is removed by anodic dissolution. This review examines 150 publications from 1992 to 2026 on hybrid and assisted machining, tool electrode design and fabrication, and electrolyte flow control. The literature was drawn from major scientific databases and includes journal articles, conference papers, and book chapters, with an emphasis on precision machining of difficult-to-machine materials. In contrast to previous reviews, this work compares the reported performance ranges, limitations, and application scenarios of five principal micro-ECM strategies: vibration-assisted, laser-assisted, jet-based, magnetic-field-assisted, and through-mask machining. It also evaluates tool electrode materials, structural designs, and fabrication methods, including abnormal wear mechanisms in silicon electrodes. Different electrolyte systems and approaches to controlling the inter-electrode flow field are discussed. The review highlights unresolved challenges, including limited understanding of multiphysics interactions, the trade-off between machining localization and mass transport, wear of non-metallic conductive electrodes, and the limited maturity of industrial applications. Priorities for future research include multiscale modeling, in situ measurement, closed-loop process control, and the development of more sustainable electrolytes. These insights may support further advances in micro-ECM and its industrial implementation.