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The swift and automated design of antennas remains a challenging aspect in research due to the specific design needs for individual applications. Alterations in resonance frequency or boundary conditions necessitate time-consuming re-designs. Though the application of evolutionary optimization and generative methods in general to antenna design has seen success, it has been mostly restricted to two-dimensional structures. In this work, we present an approach for designing three-dimensional antennas using a genetic algorithm coupled with a region-growing algorithm - to ensure manufacturability - implemented in Matlab manufactured via laser powder bed fusion (LPBF). As a simulation tool for optimization CST is used. The antenna has been optimized in a completely automated manner and was produced using the metal 3D printing technology LPBF and aluminium based AlSi10Mg powder. The presented concept, which builds upon previous two-dimensional techniques, allows for significant flexibility in design, adapting to changing boundary conditions, and avoiding the geometric restrictions seen in prior methods. The optimized antenna has a size of 3.01 cm &#x00D7; 3.43 cm &#x00D7; 1.67 cm and was measured in an anechoic chamber. According to measurements a minimum reflection coefficient of -19.95 dB at 2.462 GHz and a bandwidth of 308.8MHz are observed. CST simulation results predict an efficiency of 98.91% and the maximum antenna gain is measured at 2.45 GHz to be 3.27 dBi. Simulations made with CST and Ansys HFSS and measurements are in excellent agreement with a deviation of the resonance frequency of only 0.13%, thus further establishing genetic algorithms as a highly viable option for the design of novel antenna structures.

Evolutionary Optimized 3D WiFi Antennas Manufactured via Laser Powder Bed Fusion