Controllable helical deformations on printed anisotropic composite soft actuators

Abstract

Helical shapes are ubiquitous in both nature and engineering. However, the development of soft actuators and robots that mimic helical motions has been hindered primarily due to the lack of efficient modeling approaches that take into account the material anisotropy and the directional change of the external loading point. In this work, we present a theoretical framework for modeling controllable helical deformations of cable-driven, anisotropic, soft composite actuators. The framework is based on the minimum potential energy method, and its model predictions are validated by experiments, where the microarchitectures of the soft composite actuators can be precisely defined by 3D printing. We use the developed framework to investigate the effects of material and geometric parameters on helical deformations. The results show that material stiffness, volume fraction, layer thickness, and fiber orientation can be used to control the helical deformation of a soft actuator. In particular, we found that a critical fiber orientation angle exists at which the twist of the actuator changes the direction. Thus, this work can be of great importance for the design and fabrication of soft actuators with tailored deformation behavior.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 30, 2018
Source ID
10.1063/1.5025370

Entities

People

  • Ahmad Serjouei
  • Dong Wang
  • Guoying Gu
  • Ling Li
  • Longteng Dong
  • Oliver Weeger
  • Qi Ge

Organizations

  • Digital Manufacturing and Design Centre, Singapore University of Technology and Design
  • National Natural Science Foundation of China
  • Office of Naval Research Global
  • Shanghai Jiao Tong University
  • Shanghai Municipal Science and Technology Commission

Tags

Fields of Study

  • Physics

Readers

  • Materials Science (Mechanical Engineering).
  • Nanocomposite Materials Science
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • Autonomy