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Applied Composite Materials

Erschienen in:

25.08.2022

Prediction of Equivalent Elastic Modulus for Metal-Coated Lattice Based on Machine Learning

verfasst von: Yuzhe Liu, Feifan Sun, Min Chen, Jimin Xiao, Ji Li, Bin Wu

Erschienen in: Applied Composite Materials | Ausgabe 4/2023

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Abstract

As additive manufacturing and electroplating technique have progressed, metal-coated lattice material has wide applications due to its lightweight nature and designability. The resin matrix coated with metallic material may enhance mechanical performances while with economic cost and additional conductivity. However, a quick evaluation of equivalent material properties of metal-coated lattices is a challenging task due to the various geometric designs and coating parameters. In this paper, a numerical prediction approach is proposed with the combination of data acquisition from Finite Element Analysis (FEA) and the Machine Learning (ML) models. Firstly, a finite element model with hybrid solid and membrane elements was adopted to simulate the metal-coated lattice structure. Based on the homogenization theory, appropriate boundary conditions were defined for the Representative Volume Element (RVE) to evaluate the effective elastic modulus. With the limited numerical results, data amplification was implemented by using Polynomial Regression (PR). Finally, different ML algorithms were investigated. Artificial Neural Network (ANN) was verified as an efficient one with better prediction accuracy 99.97% for 4 variables. The proposed approach could give a reasonable property evaluation of metal-coated lattices avoiding repetitive tests and provide a feasible reference for the lattice design.

Vorheriger Artikel Low-Velocity Impact and Residual Compression Performance of Carbon Fiber Reinforced Composite Stiffened Plates

Nächster Artikel An Artificial Neural Network-based Approach to Predict the R-curve of Composite DCB Multidirectional Laminates

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Titel: Prediction of Equivalent Elastic Modulus for Metal-Coated Lattice Based on Machine Learning
verfasst von: Yuzhe Liu
Feifan Sun
Min Chen
Jimin Xiao
Ji Li
Bin Wu
Publikationsdatum: 25.08.2022
Verlag: Springer Netherlands
Erschienen in: Applied Composite Materials / Ausgabe 4/2023
Print ISSN: 0929-189X
Elektronische ISSN: 1573-4897
DOI: https://doi.org/10.1007/s10443-022-10061-0

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