Data mining and design of electromagnetic properties of Co/FeSi filled coatings based on genetic algorithms optimized artificial neural networks (GA-ANN)

It is difficult to establish analytical relationships between coating parameters (type and filling ratios of absorbent) and electromagnetic (EM) properties based on traditional trial-and-error methods. Data mining is expected to predict the EM properties of coatings filled with arbitrary set type/co...

Full description

Saved in:
Bibliographic Details
Published in:Composites. Part B, Engineering Vol. 226; p. 109383
Main Authors: Guan, Zhen-Jie, Li, Rui, Jiang, Jian-Tang, Song, Bo, Gong, Yuan-Xun, Zhen, Liang
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2021
Subjects:
Absorbing materials
Artificial neural networks (ANN)
Data mining
Genetic algorithms (GA)
Absorbing materials
Genetic algorithms (GA)
Data mining
Artificial neural networks (ANN)
ISSN:1359-8368, 1879-1069
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:It is difficult to establish analytical relationships between coating parameters (type and filling ratios of absorbent) and electromagnetic (EM) properties based on traditional trial-and-error methods. Data mining is expected to predict the EM properties of coatings filled with arbitrary set type/content of absorbent based on the measured parameters within a few samples, to solve this problem. In this study, a method for predicting EM properties of coatings was established based on GA-ANN algorithm. The proposed model was found be capable of predicting EM properties of coatings, not only in wide range of filling ratio but also in case of mixed absorbents. The GA-ANN model presented lower error and higher accuracy to compare with the conventional ANN methods. Additionally, GA was used to optimize the design of coatings via using of mixed absorbent aiming to obtain broadband electromagnetic absorption (EMA) characteristics. The optimized mixture coating with the thickness of 2.06 mm contained 13.5 wt% of Co and 55.6 wt% of FeSi particles, whose effective absorption bandwidth with reflection loss less than −10 dB (ERL10) could reach 9.3 GHz. [Display omitted] •A predictive model for electromagnetic properties of coating was established.•The model was built based on genetic algorithm optimized artificial neural network.•Electromagnetic property for arbitrarily C02/F25 content were accurately predicted.•A broadband absorbing coating was designed based on the predictive model.
ISSN:1359-8368
1879-1069
DOI:10.1016/j.compositesb.2021.109383