Simulation of auxetic behavior in planar random steel fiber networks

Abstract

The materials possess negative Poisson’s ratios are called auxetic materials. Although it is rarely available in the nature, they naturally occur in various organic and inorganic materials. Auxetic materials have distinct and advantageous behavior that can be exploited in different applications. Most of auxetic materials have porous structures, which make them vulnerable for structural applications. One of the best ways to overcome such a short come is to develop composites both are strong enough for structural application and auxetic for benefiting from the negative Poisson’s ratios for specific applications. It is known in literature some random fiber network of stainless steel fibers exhibit auxetic behavior. These networks can be employed in a stronger matrix in order to develop structurally robust auxetic composites. However, before such an approach can be developed, a methodology for designing fibrous networks with the desired negative Poisson's ratios must first be established. This requires a deep knowledge about the parameters affecting the auxetic behavior in these kinds of networks. In this work, we present a modelling approach to study the auxetic behavior of compressed fused fibrous networks. Finite element analyses of three-dimensional stochastic fiber networks were performed to gain insight into the effects of parameters such as network anisotropy, fiber morphology and degree of network compression on Poisson's ratio. In this study, the network compression ratio and anisotropy are found to be two key parameters playing important roles in the tailoring the auxetic behavior at fiber networks of stainless steel. In addition, one other outcome of this study is that 2D fiber network model responses in an analogous manner with 3D fiber network model. Hence, the 2D fiber network model can be employed for investigation of auxetic behavior fibrous networks instead of 3D models yielding less numerical cost. © Research India Publications.