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

Author(s): Xue, Tianju; Beatson, Alex; Chiaramonte, Maurizio; Roeder, Geoffrey; Ash, Jordan T; et al

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dc.contributor.authorXue, Tianju-
dc.contributor.authorBeatson, Alex-
dc.contributor.authorChiaramonte, Maurizio-
dc.contributor.authorRoeder, Geoffrey-
dc.contributor.authorAsh, Jordan T-
dc.contributor.authorMenguc, Yigit-
dc.contributor.authorAdriaenssens, Sigrid-
dc.contributor.authorAdams, Ryan P-
dc.contributor.authorMao, Sheng-
dc.date.accessioned2021-10-08T19:45:17Z-
dc.date.available2021-10-08T19:45:17Z-
dc.date.issued2020en_US
dc.identifier.citationXue, Tianju, Alex Beatson, Maurizio Chiaramonte, Geoffrey Roeder, Jordan T. Ash, Yigit Menguc, Sigrid Adriaenssens, Ryan P. Adams, and Sheng Mao. "A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation." Soft Matter 16, no. 32 (2020): 7524-7534. doi:10.1039/D0SM00488Jen_US
dc.identifier.issn1744-683X-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr19z5h-
dc.descriptionSupplementary Information: http://www.rsc.org/suppdata/d0/sm/d0sm00488j/d0sm00488j1.pdfen_US
dc.description.abstractCellular 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.en_US
dc.format.extent7524 - 7534en_US
dc.languageengen_US
dc.language.isoen_USen_US
dc.relation.ispartofSoft Matteren_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleA data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformationen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1039/D0SM00488J-
dc.identifier.eissn1744-6848-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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