Parametric Optimization of Structural Frame Design for High Payload Hexacopter

For drones, the use of which has been increasing recently for load carrying, lightweight drone frame design is significant for increased flight time and payload capacity. Drones are produced in different configurations with three, four or six rotors, and in different sizes depending on the purpose of use. While agility is more important in three and four rotor drone applications, six-rotor and relatively large-bodied drones are preferred in cases such as load carrying. When the body structure has to be large, lightening the design becomes very critical. Lightweight designs can be achieved by two commonly used methods for structural optimization: topology optimization and parametric optimization. Topology optimization is an advanced method that can significantly reduce weight but is expensive and time-consuming. Parametric optimization is a more practical approach for conventional manufacturing methods and was used in this study. This study aims to first simplifying the hexacopter frame model and defining key geometric parameters for mass-decreasing optimization. Finite element analysis simulations were used to evaluate the strength and deformation of the frame under various design scenarios. The results showed that parametric optimization successfully reduced the weight of the hexacopter frame while maintaining structural integrity. The maximum Von Mises stress was found as approximately one quarter of the yield strength of the frame material. The maximum total deformation was achieved below 0.3 mm, and deformation under 1 mm is considered safe in the literature. As a result, the optimized design offers a lighter drone structure in line with conventional manufacturing methods, providing better flight time and payload capacity while maintaining cost effectiveness. In future studies, comparisons can be made based on this study by performing weight optimizations suitable for current methods such as topology optimization or generative design. the cost factor and the availability of existing production lines should be taken into consideration when comparing the mentioned methods with parametric optimization.