TOPOLOGICAL OPTIMIZATION AND ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR DESIGNING DRONE STRUCTURES

Abstract

Unmanned aerial vehicles (UAVs) have become an indispensable tool for solving a wide range of complex tasks. To improve and increase the efficiency of unmanned aerial vehicles, it is important to optimize their structural elements. Reducing weight and strategically distributing loads while maintaining structural strength and rigidity directly impacts flight endurance, stability, payload capacity, and overall airframe integrity. Integrating topological optimization with artificial intelligence enables the automated design of components tailored to specific operational requirements.

The application of an integrated approach to automate the design of small-type UAV structures and the combination of classical methods of topological optimization with artificial intelligence and additive manufacturing technologies were considered. A microdrone airframe, fabricated via 3D printing using carbon fiber-reinforced nylon (Nylon PA12 + CF15), was analyzed both before and after generative design optimization. The effectiveness of the optimization and the changes in the structure’s rigidity and stability were assessed. Optimization resulted in an approximately 12% reduction in structural mass relative to the baseline model. Structural modeling and load simulations demonstrated that the maximum displacement of the optimized model does not exceed 3 mm, compared to 9 mm for the baseline structure. The results from virtual load simulations and physical testing of the printed models confirm that structural mass can be significantly reduced while preserving the integrity and core functional performance of the airframe. The study is based on simplified loading conditions and is limited to the number of experimental samples. Furthermore, dynamic and vibrational loads were not accounted for in the current study. Future research will focus on expanding the experimental framework and conducting an in-depth analysis of combined loading conditions that significantly affect the aerodynamic performance and real-world flight envelopes of drones.