Machines · 2024 · 16 citations · 19 references
EngineeringAerospace EngineeringMaterials EngineeringAerial RoboticsAircraft DesignChassis EngineeringAerial Robot DesignAerodynamic DesignAgricultural ApplicationsChassis MaterialsComposite MaterialsLightweight Structural MaterialsAcrylonitrile Butadiene StyreneChassis DesignFlight Performance OptimizationLightweight DesignLightweight MaterialsLightweight Structure DesignStructural Efficiency OptimizationWing Weight Reduction
This research addresses the imperative challenge of a lightweight design for an Unmanned Aerial Vehicle (UAV) chassis to enhance the thrust-to-weight and power-to-weight ratios, crucial for optimal flight performance, focused on developing an intriguing lightweight yet robust quadcopter chassis. Advanced generative design techniques, integrated with topology optimization, using Autodesk Fusion 360 software (v. 16.5. 0.2083), 3D-printing methods and lightweight materials like Polylactic Acid (P.L.A.), Acrylonitrile Butadiene Styrene (A.B.S.), and Nylon 6/6 play a significant role in achieving the desired balance between structural integrity and weight reduction. The study showcases successful outcomes, presenting quadcopter chassis designs that significantly improve structural efficiency and overall performance metrics. The findings contribute to aerial robotics and hold promise for precision agriculture applications with relevant performed simulations, emphasizing the importance of tailored design methodologies for other engineering domains. In conclusion, this research provides a foundational step toward advancing drone technology, with weight reductions of almost 50%, P/W and T/W ratios increment of 6.08% and 6.75%, respectively, at least an 11.8% increment in Factor of Safety, at least a 70% reduction in stress values and reduced manufacturing time from its comparative DJI F450 drone, demonstrating the critical role of innovative design approaches in optimizing operational efficiency for targeted applications.
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Deep Generative Design: Integration of Topology Optimization and Generative Models
Sangeun Oh, Yongsu Jung, Seongsin Kim et al. · Journal of Mechanical Design · 2019 · 437 citations · Full text