A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation

Tianju Xue, Alex Beatson, Maurizio Chiaramonte, Geoffrey Roeder, Jordan T. Ash, Yigit Menguc, Sigrid Adriaenssens, Ryan P. Adams, Sheng Mao

Research output: Contribution to journalArticle

Abstract

Cellular mechanical metamaterials are a special class of materials whose mechanical properties are primarily determined by their geometry. However, capturing the nonlinear mechanical behavior of these materials, especially those with complex geometries and under large deformation, can be challenging due to inherent computational complexity. In this work, we propose a data-driven multiscale computational scheme as a possible route to resolve this challenge. We use a neural network to approximate the effective strain energy density as a function of cellular geometry and overall deformation. The network is constructed by "learning"from the data generated by finite element calculation of a set of representative volume elements at cellular scales. This effective strain energy density is then used to predict the mechanical responses of cellular materials at larger scales. Compared with direct finite element simulation, the proposed scheme can reduce the computational time up to two orders of magnitude. Potentially, this scheme can facilitate new optimization algorithms for designing cellular materials of highly specific mechanical properties. This journal is

Original languageEnglish (US)
Pages (from-to)7524-7534
Number of pages11
JournalSoft matter
Volume16
Issue number32
DOIs
StatePublished - Aug 28 2020

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Condensed Matter Physics

Fingerprint Dive into the research topics of 'A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation'. Together they form a unique fingerprint.

  • Cite this