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

• Published in: IEEE access 2023, Vol.11, p.121914-121923
• Main Authors: Mair, Dominik, Renzler, Michael, Kovar, Stanislav, Martinek, Tomas, Kadavy, Tomas, Bergmueller, Simon, Horn, Andrada, Braun, Jakob, Kaserer, Lukas
• Format: Article
• Language: English
• Subjects: additive manufacturing (AM); Anechoic chambers; Antenna design; Antenna gain; Antenna measurements; Antennas; Automation; Boundary conditions; Design; Genetic algorithms; laser powder bed fusion (LPBF); Manufacturability; Matlab; Optimization; Powder beds; Reflectance; Resonance; Simulation; Sociology; Statistics; Three dimensional printing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3328852

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 \text {cm} \times 3.43 \text {cm} \times 1.67 \text {cm} and was measured in an anechoic chamber. According to measurements a minimum reflection coefficient of \mathrm {-19.95\,\, \text {dB}} at \mathrm {2.462~ \text {G} \text { Hz} } and a bandwidth of \mathrm {308.8~ \text {M} \text { Hz} } are observed. CST simulation results predict an efficiency of \mathrm {98.91~\%} and the maximum antenna gain is measured at \mathrm {2.45~ \text {G} \text { Hz} } to be \mathrm {3.27~ \text {dB} i} . Simulations made with CST and Ansys HFSS and measurements are in excellent agreement with a deviation of the resonance frequency of only \mathrm {0.13~\%} , thus further establishing genetic algorithms as a highly viable option for the design of novel antenna structu