Design of Tuneable Multifunctioning Metamaterial Absorbers using Progressive Neural Network Metaheuristics

This paper presents a novel design optimization framework for a tuneable broadband mechanical metamaterial absorber (TBMMA).Utilizing a progressive neural network and metaheuristic algorithms, we capture the complex behaviour behind the mechanical properties and electromagnetic properties and enhance both microwave absorption and stiffness of octet-truss based metamaterial absorber.The proposed method leverages a multilayered octet truss structure with functionally graded approach, adjusting design variables such as strut diameter and aspect ratio to achieve optimal performance.This integrated approach demonstrates significant improvements in multifunctional properties with tunability through various optimization problems differed by operating frequency range, constraints for target design.