A machine learning-based method for co-design and optimization of microwave-absorbing/load-bearing multifunctional structures

Abstract

Simultaneously considering the absorption performance and load-bearing capability is a trend in the design of multifunctional structures. Nevertheless, the collaborative design and optimization involved in this process present a challenging problem. Herein, guided by multifunctionality, a lightweight microwave-absorbing/load-bearing multifunctional structure is intelligently inversely designed based on machine learning. A co-design scheme is developed to address the contradiction between the absorption performance and load-bearing performance. An approach for rapid inverse design of metamaterial absorbers containing multilayered frequency-selective surfaces (FSSs) is proposed. The simulation results obtained using multi-objective optimization based on surrogate models indicate that the optimized multifunctional structure achieves more than 90% absorption in the frequency range of 2.5 GHz to 18.0 GHz and simultaneously exhibits superior load-bearing performance with an out-of-plane Young's modulus of 334.8 MPa and an out-of-plane compressive strength of 4.95 MPa, demonstrating the effectiveness of the co-design scheme. Finally, the experimental results are analysed. This study provides a reference for co-design and multi-objective optimization of similar multifunctional structures.