Functionally graded graphene origami-enabled auxetic metamaterial beams with tunable buckling and postbuckling resistance
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- @Article{ZHAO:2022:engstruct,
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author = "Shaoyu Zhao and Yingyan Zhang and Helong Wu and
Yihe Zhang and Jie Yang",
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title = "Functionally graded graphene origami-enabled auxetic
metamaterial beams with tunable buckling and
postbuckling resistance",
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journal = "Engineering Structures",
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year = "2022",
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volume = "268",
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pages = "114763",
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keywords = "genetic algorithms, genetic programming, Functionally
graded beam, Mechanical metamaterial, Negative
Poisson's ratio, Graphene origami, GP-assisted
micromechanical model",
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ISSN = "0141-0296",
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URL = "https://www.sciencedirect.com/science/article/pii/S0141029622008501",
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DOI = "doi:10.1016/j.engstruct.2022.114763",
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abstract = "Auxetic metamaterials have emerged as novel advanced
materials with unique physical and mechanical
properties that conventional materials do not possess.
This paper examines the buckling and postbuckling
properties of functionally graded (FG) graphene origami
(GOri)-enabled auxetic metallic metamaterial (GOEAM)
beams. The beam is comprised of multiple GOEAM layers
with GOri content varying in layer-wise patterns to
realize gradient-changing Poisson's ratio and stiffness
coefficient through the beam thickness. The material
properties of each GOEAM layer are estimated by the
genetic programming (GP)-assisted micromechanical
models. The first-order shear deformation theory and
von Karman type nonlinearity are employed to derive the
nonlinear governing equations that are numerically
solved by the differential quadrature method (DQM).
Numerical investigations are carried out with the main
focus on the impacts of GOri content, distribution
pattern, folding degree, and temperature on the
buckling and postbuckling behaviors of FG metamaterial
beams. The theoretical results show that GOri is
capable of contributing to the formation of auxetic
metal metamaterial, leading to the tunable bucking and
postbuckling properties of FG beams, which sheds
significant insights into the design of
high-performance structures",
- }
Genetic Programming entries for
Shaoyu Zhao
Yingyan Zhang
Helong Wu
Yihe Zhang
Jie Yang
Citations