The elastic properties of composites reinforced by a 3D Voronoi fibre network with or without missing fibres

TL;DR

This study investigates the elastic properties of composites reinforced by 3D Voronoi fibre networks, highlighting how network regularity and minor defects influence stiffness, with potential applications in aerogel simulation.

Contribution

It provides a systematic analysis of how Voronoi fibre network regularity affects composite stiffness and demonstrates robustness against fibre defects, offering new insights for composite design.

Findings

Higher network regularity increases Young's modulus.

Minor defects (20%) cause less than 6.5% reduction in stiffness.

Voronoi fibre networks outperform conventional composites in stiffness.

Abstract

Many composite materials, both natural and fabricated, process a Voronoi like architecture or microstructure. Furthermore, the stochasticity and connectivity of Voronoi tessellation endow the composite materials constructed by this kind of structures a wide range of desired properties including high and tuneable stiffness, high strength and good manufacturability. Thus, Voronoi-based fibre network structures are regarded as promising designs of composite reinforcements, as well as powerful tools in composite mechanics simulations. In this paper, the elastic properties of composites reinforced by a 3D Voronoi fibre network are systemically investigated based on the precise control of the Voronoi cell regularity. The regularity of the reinforcement fibre networks, according to our definition, is found positively related to the Young's moduli of the composites. Interestingly, 20% percent of defects in total reinforcement fibres only causes a less than 6.5% of Young's moduli drop in Voronoi fibre network reinforced composite. The Voronoi fibre network reinforced composites also shows higher Young's moduli than those of conventional composites with discrete reinforcements. The possibility of simulating aerogels by Voronoi fibre network structures are also presented.