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

Yuzhe Liu, Feifan Sun, Min Chen*, Jimin Xiao, Ji Li, Bin Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)1207-1229
Number of pages23
JournalApplied Composite Materials
Issue number4
Publication statusPublished - 2022


  • Equivalent properties prediction
  • Lattice structure
  • Machine learning
  • Metal-coated


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