Generative Design for 3D Printing of Advanced Aerial Drones

In the current study completed in the Facility for Research on Aerospace Materials and Engineered Structures (FRAMES), the feasibility of implementing generative design as a means of optimizing advanced aerial drone structures was explored. By conducting relevant literature review, theoretical investigations, and experimentation, generative design demonstrated its efficacy as a design tool for various engineering structure applications. Generative design uses a series of artificial intelligence (AI) algorithms to compute various potential geometries for optimized load distribution; it is a powerful tool that provides fast and efficient topology optimized structures. This paper offers insight on the intricacies of unmanned aerial vehicle (UAV) design and discusses the various complications and advantages of using various drone geometries, manufacturing techniques, and materials. The interdependencies between geometry, manufacturing method, and material are also discussed. As such, the optimal frame type, manufacturing method, and material for optimized drone frame designs was found to be square-type, 3D-printing (MEX/FFF), and PEEK respectively. A generatively designed drone frame was created in Fusion 360 and analyzed using its own finite element analysis (FEA) capabilities; later, physical prototyping and testing verified the results gathered from FEA. This study attempts to re-introduce the feasibility and applicability of generative design in a sophisticated manner with the intention of closing gaps in novel research of drone frame optimization.