Automated vibrational design and natural frequency tuning of multi-material structures

Natural frequency tuning is a vital engineering problem. Every structure has natural frequencies, where vibrational loading at nearby frequencies excite the structure. This causes the structure to resonate, oscillating until energy is dissipated through friction or structural failure. Examples of fragility and distress from vibrational loading include civil structures during earthquakes or aircraft rotor blades. Tuning the structure's natural frequencies away from these vibrations increases the structure's robustness. Conversely, tuning towards the frequencies caused by vibrations can channel power into energy harvesting systems. Despite its importance, natural frequency tuning is often performed ad-hoc, by attaching external vibrational absorbers to a structure. This is usually adequate only for the lowest ("fundamental") resonant frequencies, yet remains standard practice due to the unintuitive and difficult nature of the problem. Given Evolutionary Algorithms' (EA's) ability to solve these types of problems, we propose to approach this problem with the EA CPPN-NEAT to evolve multi-material structures which resonate at multiple desired natural frequencies without external damping. The EA assigns the material type of each voxel within the discretized space of the object's existing topology, preserving the object's shape and using only its material composition to shape its frequency response.