Generative AI for 3D Printed Antenna Design

This research explores the integration of generative artificial intelligence (AI) with a physics-informed particle swarm optimizer (PSO) to develop 3D printable microstrip patch antennas. A neural network was trained on a dataset of microstrip patch antenna geometries and their corresponding performance metrics: return loss and gain. The PSO used a fitness function prioritizing low return loss in potential antennas, eventually yielding novel antenna geometries with parasitic components. 3D printing constraints were also hard coded into the framework, thus preventing any geometries being generated that cannot be fabricated. When simulated using Ansys HFSS, the AI generated microstrip patch antennas exceeded the target performance metrics, as the designed antenna demonstrated a gain increase of 0.75 dB above the target, and an S11 improvement of 6.48 dB beyond the target of -20 dB. These results illustrate the utility of AI with an optimization algorithm in generating antenna geometries that not only perform well, but are readily fabricable using hybrid 3D printing. The use of hybrid 3D printing in this area reduces the limitations found in traditional manufacturing and allows for greater exploitation of the degrees of freedom offered by the additive manufacturing technology. This will allow for rapid prototyping and a more explorative examination of the 3D design space in the future.