Tunable nonlinear bending behaviors of functionally graded graphene origami enabled auxetic metamaterial beams

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@Article{ZHAO:2022:compstruct,

• author = "Shaoyu Zhao and Yingyan Zhang and Helong Wu and Yihe Zhang and Jie Yang and Sritawat Kitipornchai",
• title = "Tunable nonlinear bending behaviors of functionally graded graphene origami enabled auxetic metamaterial beams",
• journal = "Composite Structures",
• volume = "301",
• pages = "116222",
• year = "2022",
• ISSN = "0263-8223",
• DOI = "doi:10.1016/j.compstruct.2022.116222",
• URL = "https://www.sciencedirect.com/science/article/pii/S0263822322009540",
• keywords = "genetic algorithms, genetic programming, Functionally graded beam, Negative Poisson's ratio, Mechanical metamaterial, Nonlinear bending, GP-assisted micromechanical model",
• abstract = "This paper investigates tunable nonlinear bending behaviors of functionally graded composite beams made of graphene origami (GOri)-enabled auxetic metal metamaterials (GOEAMs) within the theoretical framework of the first-order shear deformation theory and von Karman type nonlinearity. The beam is comprised of multiple GOEAM layers with GOri content and folding degree being variables to effectively control its auxetic property that is graded from layer to layer across its thickness direction. Our developed genetic programming (GP)-assisted micromechanical models are used to estimate the position- and temperature-dependent Poisson's ratio and other material properties of each GOEAM layer in the beam. The nonlinear governing equations of the FG-GOEAM beam are derived by the principle of virtual work and numerically solved by the differential quadrature (DQ) method. A detailed parametric investigation is conducted to examine the effects of GOri content, folding degree, and temperature on the tunability of the nonlinear bending deflection and normal stress of the FG metamaterial beam. Numerical results offer significant insights into the design of FG-GOEAM beam structures with enhanced bending performances",

}

Genetic Programming entries for Shaoyu Zhao Yingyan Zhang Helong Wu Yihe Zhang Jie Yang Sritawat Kitipornchai

Citations