Generative design and structural optimization of a hexacopter frame for agricultural UAV spraying

This study presents a generative design-based optimization of a hexacopter drone frame intended for agricultural spraying applications. Utilizing artificial intelligence-driven algorithms within Autodesk Fusion 360, the research explores multiple frame configurations based on structural constraints and performance objectives. Finite Element Method (FEM) simulations, conducted through both ANSYS and Fusion 360, modeled dynamic operational scenarios such as landing impacts and aerial collisions. These simulations enabled the identification of overstressed regions, leading to iterative material removal and geometric refinement. The finalized frame design achieved a mass of 2.784 kg, translating to a 7.2% to 38.1% reduction compared to traditional 10-liter agricultural drone frames, while maintaining a high safety factor range of 9.60 to 15 and a maximum displacement of just 1.288 mm. Furthermore, flight performance analysis indicated a linear reduction in throttle requirement from 80% to 32.6% as tank weight decreased from 16.88 kg to 6.88 kg, thereby extending flight duration from 6 to 14.6 minutes. These results demonstrate the effectiveness of generative design for developing structurally efficient and operationally robust UAV frames. Future research will explore foldable design strategies to further enhance portability and field adaptability.