Optimization of Generatively Encoded Multi-Material Lattice Structures for Desired Deformation Behavior

Abstract

Natural systems achieve favorable mechanical properties through coupling significantly different elastic moduli within a single tissue. However, when it comes to man-made materials and structures, there are a lack of methods which enable production of artifacts inspired by these phenomena. In this study, a method for design automation based on alternate deposition of soft and stiff struts within a multi-material 3D lattice structure with desired deformation behavior is proposed. These structures, once external forces are applied, conform to the geometry given in advance. For that purpose, a population-based algorithm was proposed and integrated with a multi-material physics simulator. To reduce the amount of data processed during optimization, a generative encoding method based on discrete cosine transform (DCT) was proposed. This enabled a compressed topological description and promoted symmetry in material distribution. The simulation results showed different three-dimensional lattice structures designed with proposed algorithm to meet a set of desired deformation behaviors. The relation between residual deformation error, targeted deformation geometry, and material distribution is discussed.

Fused Deposition Modelling for 3D Printing of Soft Anthropomorphic Actuators

“…Unlike conventional robots which are functional in a highly defined environment, soft robots provide much more flexibility, they are resistant to impacts, uncertainties, and can operate in loosely defined environments [9], [10]. In the case of multi-material design of soft robots' body, increased flexibility in functional preprograming is achievable [11]. When it comes to grasping, soft robots conform to shape, eliminating occurrence of concentrated pressure that can lead to object damage.…”