Materials Research Express (Jan 2020)
Effects of cell aspect ratio and relative density on deformation response and failure of honeycomb core structure
Abstract
The extensive applications of honeycomb (HC) core in sandwich structures necessitates the influence of the cellular geometry and cell wall base material on the mechanical response to be quantified. In this respect, this paper establishes the mechanics of the deformation and the failure processes of the HC core under the out-of-plane compressive, tensile, and shear loading. The corresponding mechanical properties are determined and the mechanisms of failure of the HC core structure are identified. The influence of the relative density ( ρ */ ρ _s ) and the cell aspect ratio ( H / c ) of the hexagonal HC core on the compressive deformation response, the out-of-plane properties and the characteristic dissipation energy density (DED) of the structure is established. Results show that the compressive strength increases exponentially from 1.5 to 10.6 MPa over the relative density range of 0.028 ≤ ( ρ */ ρ _s ) ≤ 0.125. The out-of-plane shear modulus, G _13 and G _23 are 33.9 and 58.2 MPa, while the shear strength, τ _13 and τ _23 are 1.07 and 2.03 MPa, respectively. The HC core with a low aspect ratio ( H / c < 2.64) failed due to the early debonding of the double-wall hexagonal cells, while at H / c ≥ 2.64, by elastic buckling of the cells. A phenomenological model is formulated to highlight the combined effects of both parameters on the compressive strength ( σ _c ) of the HC cores, covering the range of 0.028 ≤ ( ρ */ ρ _s ) ≤ 0.056 and 2.5 ≤ (H/c) ≤ 5.62. Furthermore, the characteristic dissipation energy density (DED) under the out-of-plane compression varies linearly within the range of 2.5 < (H/c) < 5.62 for the HC core with ρ */ ρ _s = 0.056. The HC core with H / c = 3.96, but with twice higher ρ */ ρ _s exhibits about twice larger DED. These resulting properties and failure mechanisms of the anisotropic paper-based HC core are useful for the validation of the predictive computational models.
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