Honeycomb metastructures are widely used in electromagnetic wave absorption applications due to their lightweight and high-strength properties.While geometric modifications can further enhance microwave absorption,the...Honeycomb metastructures are widely used in electromagnetic wave absorption applications due to their lightweight and high-strength properties.While geometric modifications can further enhance microwave absorption,the unclear relationships between structural parameters,electromagnetic response,and mechanical performance present challenges for optimizing these structures to achieve both absorption and mechanical performance.This study introduces an automated framework for the bi-objective optimization of hybrid geometry honeycomb metastructures(HGHMs),fabricated with a graphene conductive coating and photosensitive resin for the honeycomb substrate,designed to improve both microwave absorption and mechanical resistance.By integrating artificial intelligence(AI),parametric modeling,and finite element analysis,a robust system was developed to explore the design space.Two optimized HGHM configurations were identified:One prioritizes microwave absorption with a-10 dB bandwidth of 6.1–18.0 GHz,a-15 dB bandwidth of 6.9–16.3 GHz,and a compressive Young's modulus of E=123 MPa,while the other balances absorption performance(-10 dB bandwidth:5.7–18.0 GHz)and mechanical robustness with E=638 MPa.Experimental validation confirmed the simulation results,and sensitivity analysis revealed the relationship between structural design,absorption,and deformation resistance.Based on a highaccuracy neural network surrogate model for the prediction of reflection loss curves,differential evolution was employed to suggest geometric parameters that lead to desired reflection loss curves.These results underscore the transformative potential of AI-based optimization for the rapid,automated,and customized design of multifunctional metastructures.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB3707800)the“Artificial Intelligence Empowering Scientific Research Plan”initiative of the Shanghai Municipal Education Commissionthe National Natural Science Foundation of China(Grant Nos.12072179,12421002,52231007,12327804)。
文摘Honeycomb metastructures are widely used in electromagnetic wave absorption applications due to their lightweight and high-strength properties.While geometric modifications can further enhance microwave absorption,the unclear relationships between structural parameters,electromagnetic response,and mechanical performance present challenges for optimizing these structures to achieve both absorption and mechanical performance.This study introduces an automated framework for the bi-objective optimization of hybrid geometry honeycomb metastructures(HGHMs),fabricated with a graphene conductive coating and photosensitive resin for the honeycomb substrate,designed to improve both microwave absorption and mechanical resistance.By integrating artificial intelligence(AI),parametric modeling,and finite element analysis,a robust system was developed to explore the design space.Two optimized HGHM configurations were identified:One prioritizes microwave absorption with a-10 dB bandwidth of 6.1–18.0 GHz,a-15 dB bandwidth of 6.9–16.3 GHz,and a compressive Young's modulus of E=123 MPa,while the other balances absorption performance(-10 dB bandwidth:5.7–18.0 GHz)and mechanical robustness with E=638 MPa.Experimental validation confirmed the simulation results,and sensitivity analysis revealed the relationship between structural design,absorption,and deformation resistance.Based on a highaccuracy neural network surrogate model for the prediction of reflection loss curves,differential evolution was employed to suggest geometric parameters that lead to desired reflection loss curves.These results underscore the transformative potential of AI-based optimization for the rapid,automated,and customized design of multifunctional metastructures.
