3D printable strain rate-dependent machine-matter
Abstract
Machine-matter, of which mechanical metamaterials and meta-devices are important sub-categories, is emerging as a major paradigm for designing advanced functional materials. Various exciting applications of these concepts have been recently demonstrated, ranging from exotic mechanical properties to device-like and adaptive functionalities. The vast majority of the studies published to date have, however, focused on the quasi-static behavior of such devices, neglecting their rich dynamic behavior. Recently, we proposed a new class of strain rate-dependent mechanical metamaterials that are made from bi-beams (i.e., viscoelastic bilayer beams). The buckling direction of such bi-beams can be controlled with the applied strain rate. The proposed approach, however, suffers from a major limitation: 3D printing of such bi-beams with such a 'strong' differential strain rate-dependent response is very challenging. Here, we propose an alternative approach that only requires a 'weak' differential response and a rationally designed geometric artifact to control the buckling direction of bi-beams. We present an analytical model that describes the landscape of all possible combinations of geometric designs and hyperelastic as well as viscoelastic properties that lead to the desired strain rate-dependent switching of the buckling direction. We also demonstrate how multi- and single-material 3D printing techniques can be used to fabricate the proposed bi-beams with microscale and submicron resolutions. More importantly, we show how the requirement for a weak differential response eliminates the need for multi-material 3D printing, as the change in the laser processing parameters is sufficient to achieve effective differential responses. Finally, we use the same 3D printing techniques to produce strain rate-dependent gripper mechanisms as showcases of potential applications.
Cite
@article{arxiv.2206.15168,
title = {3D printable strain rate-dependent machine-matter},
author = {Shahram Janbaz and Daniel Fan and Mahya Ganjian and Teunis van Manen and Urs Staufer and Amir A. Zadpoor},
journal= {arXiv preprint arXiv:2206.15168},
year = {2022}
}