Designing a carbon-based electromagnetic absorber textile material using DS-ABC optimization.

Abstract

In this study, lightweight and flexible multilayer radar absorbing materials (MRAMs) are developed using carbon-based textile coatings and optimized through a doublestage Artificial Bee Colony (DS-ABC) algorithm. Polyester and cotton fabrics are dip-coated with polyvinyl alcohol (PVA) solutions containing 5 wt% multi-walled carbon nanotubes (MWCNTs) and 10-30 wt% graphite, resulting in eight distinct material samples. Characterization of the samples is performed via vector network analyzer (VNA) within the frequency range of 2-18 GHz and their electromagnetic properties are compiled into a database for algorithm-based optimization. The DS-ABC algorithm is employed to determine the optimum number of layers, their sequence, and individual thicknesses. Unlike traditional approaches that co-define material types and thicknesses in a fixed parameter space, this method evaluated these attributes independently, allowing for a more comprehensive search of design configurations. As a result, an optimized two-layer MRAM with a total thickness of approximately 7 mm is obtained, consisting of a 3.69 mm cotton fabric coated with 30 wt% graphite and a 3.35 mm polyester fabric coated with 5 wt% MWCNT. The final design achieved an average reflection coefficient below -10 dB across the full frequency band and across incidence angles from 0° to 40°, under transverse electric (TE) and transverse magnetic (TM) polarizations. These findings highlight the potential of carbon-based coated textiles as effective, conformal, and manufacturable EM absorbers for next-generation wearable and stealth applications.