The emergence of sequential buckling in reconfigurable hexagonal networks embedded into soft matrix

Pavel I. Galich, Aliya Sharipova, Slava Slesarenko

Research output: Contribution to journalArticlepeer-review

Abstract

The extreme and unconventional properties of mechanical metamaterials originate in their sophisticated internal architectures. Traditionally, the architecture of mechanical metamaterials is decided on in the design stage and cannot be altered after fabrication. However, the phenomenon of elastic instability, usually accompanied by a reconfiguration in periodic lattices, can be harnessed to alter their mechanical properties. Here, we study the behavior of mechanical metamaterials consisting of hexagonal networks embedded into a soft matrix. Using finite element analysis, we reveal that under specific conditions, such metamaterials can undergo sequential buckling at two different strain levels. While the first reconfiguration keeps the periodicity of the metamaterial intact, the secondary buckling is accompanied by the change in the global periodicity and formation of a new periodic unit cell. We reveal that the critical strains for the first and the second buckling depend on the metamaterial geometry and the ratio between elastic moduli. Moreover, we demonstrate that the buckling behavior can be further controlled by the placement of the rigid circular inclusions in the rotation centers of order 6. The observed sequential buckling in bulk metamaterials can provide additional routes to program their mechanical behavior and control the propagation of elastic waves.

Original languageEnglish
Article number2038
JournalMaterials
Volume14
Issue number8
DOIs
StatePublished - 2 Apr 2021

Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics

Keywords

  • Buckling
  • Elastic wave propagation
  • Instabilities
  • Mechanical metamaterials
  • Reconfiguration
  • Sequential buckling

Fingerprint

Dive into the research topics of 'The emergence of sequential buckling in reconfigurable hexagonal networks embedded into soft matrix'. Together they form a unique fingerprint.

Cite this