Design and Modelling of Carbon Fiber Grid Structure based Carbon/Epoxy Composites for Enhanced Microwave Absorbing Properties

Arya, Aayushi; Srikanth, I.

doi:10.5185/amlett.2020.111577

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Microwave absorbing composites have become an important field of research due to their growing applications. This field has seen a variety of research articles dedicated to the experimental studies of the absorbing properties of microwave absorbers [1][2][3]. There are many theoretical works also, modeling the permittivity and permeability of microwave absorbers [4][5][6][7], but they do not analyze their atomic fundamentals.…”

Section: Introductionmentioning

confidence: 99%

Development of a Graphical User Interface for Reflection Loss Calculation in Perovskite-RGO based Microwave Absorbing Composites

Arya

2023

Eng. Technol. Appl. Sci. Res.

Self Cite

View full text Add to dashboard Cite

In this paper, a novel method is investigated wherein the theoretical and mathematical analysis of the perovskite-Reduced Graphene Oxide (RGO) based composite microwave absorber is used to form a machine learning model using linear regression to predict the reflection loss and the effective dielectric permittivity of a selected perovskite compound in an RGO-based composite. At first, the theoretical derivation is carried out to find a mathematical relationship between the reflection loss and the dielectric permittivity of the composite and the cationic radii of the perovskite structure, which is then used to form the base for the machine learning model to directly calculate the microwave absorption characteristics from the atomic parameters of the given composite structure. Linear regression is used for the machine learning algorithm which is verified with an R2 of 0.869 with the atomic radii as the input parameters. The model is further used to develop a Graphical User Interface (GUI) to make the prediction more appealing and user-friendly. The current paper provides a new approach to the integration of theoretical knowledge with advanced computing tools to form innovative predictive tools for current microwave-absorbing materials.

show abstract