Fig. 1. Discrete Interlocking Materials are governed by strongly coupled, highly anisotropic, and asymmetric deformation limits.
Our method is able to capture and reproduce these effects for many types of interlocking materials (a). Using native-scale
simulations as a basis (b), we construct macromechanical deformation limits on bending and stretching (c) which we use to
develop an efficient macro-scale simulation model (d).
Abstract
We present a method for computational modeling, mechanical characterization,
and macro-scale simulation of discrete interlocking materials (DIM)---3D-printed
chainmail fabrics made of quasi-rigid interlocking elements. Unlike conventional
elastic materials for which deformation and restoring force are directly coupled,
the mechanics of DIM are governed by contacts between individual elements that
give rise to anisotropic deformation constraints. To model the mechanical
behavior of these materials, we propose a computational approach that builds on
three key components. (a): we explore the space of feasible deformations using
native-scale simulations at the per-element level. (b): based on this simulation
data, we introduce the concept of strain-space boundaries to represent deformation
limits for in- and out-of-plane deformations, and (c): we use the strain-space
boundaries to drive an efficient macro-scale simulation model based on homogenized
deformation constraints. We evaluate our method on a set of representative discrete
interlocking materials and validate our findings against measurements on physical prototypes.
We would like to thank Charles Gingras for the valuable discussion, and Ronan Hinchet
for helping with the measuring setup and fabricating some of the physical prototypes.
We are also grateful to the anonymous reviewers for their valuable comments. This work
was supported by the Discovery Grants Program and the Discovery Accelerator Awards
program of the Natural Sciences and Engineering Research Council of Canada (NSERC).
Computing equipment has been funded through an infrastructure grant from the Canada
Foundation for Innovation (CFI). This work was also supported by the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation program
(grant agreement No. 866480), and the Swiss National Science Foundation through SNF
project grant 200021_200644.
