Optimizing properties on the critical rigidity manifold of underconstrained central-force networks

TL;DR

This paper introduces a framework for designing underconstrained central-force networks that can tune their rigidity and optimize multiple mechanical properties, enabling advanced multifunctional metamaterials.

Contribution

It reveals the critical rigidity manifold in such networks and provides a method to generate structures on this manifold that optimize specific objectives.

Findings

Networks on the critical manifold can maximize bulk stiffness.

Networks can be designed to minimize length variance.

The framework enables multifunctional property optimization.

Abstract

Our goal is to develop a design framework for multifunctional mechanical metamaterials that can tune their rigidity while optimizing other desired properties. Towards this goal, we first demonstrate that underconstrained central force networks possess a critical rigidity manifold of codimension one in the space of their physical constraints. We describe how the geometry of this manifold generates a natural parameterization in terms of the states of self-stress, and then use this parameterization to numerically generate disordered network structures that are on the critical rigidity manifold and also optimize various objective functions, such as maximizing the bulk stiffness under dilation, or minimizing length variance to find networks that can be self-assembled from equal-length parts. This framework can be used to design mechanical metamaterials that can tune their rigidity and also exhibit other desired properties.