Analytic analysis of auxetic metamaterials through analogy with rigid link systems
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In recent years many structural motifs have been designed with the aim of creating auxetic metamaterials. One area of particular interest in this subject is the creation of auxetic material properties through elastic instability. Such metamaterials switch from conventional behaviour to an auxetic response for loads greater than some threshold value. This paper develops a novel methodology in the analysis of auxetic metamaterials which exhibit elastic instability through analogy with rigid link lattice systems. The results of our analytic approach are confirmed by finite-element simulations for both the onset of elastic instability and post-buckling behaviour including Poisson’s ratio. The method gives insight into the relationships between mechanisms within lattices and their mechanical behaviour; as such, it has the potential to allow existing knowledge of rigid link lattices with auxetic paths to be used in the design of future buckling induced auxetic metamaterials.
Rayneau-Kirkhope , D , Zhang , C , Theran , L S & Dias , M 2018 , ' Analytic analysis of auxetic metamaterials through analogy with rigid link systems ' , Proceedings of the Royal Society A - Mathematical, Physical & Engineering Sciences , vol. 474 , no. 2210 , 20170753 . https://doi.org/10.1098/rspa.2017.0753
Proceedings of the Royal Society A - Mathematical, Physical & Engineering Sciences
© 2018, the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1098/rspa.2017.0753
DescriptionD.R.-K. acknowledges funding support from Academy of Finland and Aalto Science Institute. C.Z. acknowledges funding from Aalto Science Institute. L.T. acknowledges funding from Aalto Science Institute thematic program ‘Challenges in large geometric structures and big data’.
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