Tailorable elasticity of cantilever using spatio-angular functionally graded biomimetic scales

Keywords

Biomimetic scales; Functionally graded material; Nonlinear elasticity; Stiffness tailorability

Abstract

Cantilevered beams are of immense importance as structural and sensorial members for a number of applications. Endowing tailorable elasticity can have wide ranging engineering ramification. Such tailorability could be possible using some type of spatial gradation in the beam’s material or cross section. However, these often require extensive additive and subtractive material processing or specialized casts. Herein, we demonstrate an alternative bio-inspired mechanical pathway, which is based on exploiting the nonlinearity that would arise from a functionally graded (FG) distribution of biomimetic scales on the surface using an analytical approach. This functional gradation is geometrically sourced and could arise from either spatial or angular gradation of scales. We analyze such FG cantilever beams under different loading conditions including point loading at the free end, uniform traction, linearly distributed traction, and concentrated moment loading at the free end. In comparison with uniformly distributed scales for all cases of the loading addressed, we find significant differences in bending stiffness for both spatial and angular gradations. Spatial and angular functional gradations share some universality but also sharp contrasts in their effect on the underlying beam. We also quantify the landscape of spatio-angular tailorability on stiffness gains. We compare our models with select experiments for validation. This highlights that a combination of both types of gradation in the structure can be used to alter stiffness and therefore offer a pathway to tailor the elasticity of a cantilever beam relatively easily. These results demonstrate an architected framework for designing and optimizing scale-covered FG beams.

Date Created

October 2019

https://works.bepress.com/hossein-ebrahimi/14/download/

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