Auxetic metamaterials from disordered networks

TL;DR

This paper demonstrates that by incorporating realistic forces and boundary conditions into disordered networks, one can precisely tune their mechanical properties, including achieving arbitrary Poisson's ratios, and validates these designs through physical experiments.

Contribution

Introduces a realistic network model with angle-bending forces and boundary conditions, enabling precise tuning of mechanical responses, including auxetic behavior, through pruning and optimization.

Findings

Poisson's ratio can be tuned to arbitrary values.

Physical networks behave as predicted by the model.

Optimization enhances auxetic behavior beyond homogeneous networks.

Abstract

Recent theoretical work suggests that systematic pruning of disordered networks consisting of nodes connected by springs can lead to materials that exhibit a host of unusual mechanical properties. In particular, global properties such as the Poisson's ratio or local responses related to deformation can be precisely altered. Tunable mechanical responses would be useful in areas ranging from impact mitigation to robotics and, more generally, for creation of metamaterials with engineered properties. However, experimental attempts to create auxetic materials based on pruning-based theoretical ideas have not been successful. Here we introduce a new and more realistic model of the networks, which incorporates angle-bending forces and the appropriate experimental boundary conditions. A sequential pruning strategy of select bonds in this model is then devised and implemented that enables engineering of specific mechanical behaviors upon deformation, both in the linear and non-linear regimes. In particular, it is shown that the Poisson's ratio can be tuned to arbitrary values. The model and concepts discussed here are validated by preparing physical realizations of the networks designed in this manner, which are produced by laser cutting two-dimensional sheets and are found to behave as predicted. Furthermore, by relying on optimization algorithms, we exploit the networks' susceptibility to tuning to design networks that posses a distribution of stiffer and more compliant bonds, and whose auxetic behavior is even greater than that of homogeneous networks. Taken together, the findings reported here serve to establish that pruned networks represent a promising platform for the creation of novel mechanical metamaterials.