Design tools and predictions for nonlinear elastic solids in soft machine design

Soft machine design is a new frontier for using and designing rheologically complex materials, however we lack a systematic design toolbox for such efforts. We study existing design tools (Chapter 1), identify limitations (Chapter 1), and present new design tools (Chapter 2) for rheologically-complex soft solids. We identify material properties and features that may be vital for optimal soft machine performance, such as intrinsic nonlinearities, variable stiffness, and strain to break. Keeping these properties in mind, we explore new design-based organizations of knowledge, including design motivated constitutive modeling and Ashby-style material selection charts. As a result, we develop new Ashby-style diagrams with properties not typically reported, include soft materials that tend to be missing from material databases, and organize knowledge in a way that streamlines material selection for soft machines. Soft robot actuators, grippers, and skins, as well as medical devices, are just a few of the many soft systems that may benefit from the proposed soft machine design tools. After discussing limitations of the current state of soft machine design and making contributions to soft machine design tools that support the `design with' materials, we study mathematical models to support the `design of' nonlinear elastic functional solids for soft machines (Chapter 3). We study the behavior of magnetorheological elastomers (MRE's), and specifically the effect that nonlinearity and softness have on the variable shear stiffness response of MRE's. We find surprising results, where a higher degree of nonlinearity can either stiffen or soften the variable shear stiffness response depending on competing magnetic and nonlinear elastic free energy contributions. We define three different regimes where we expect the elastic nonlinearity of a MRE to have a different effect on the variable shear stiffness of the composite. These functional materials are building blocks for soft machine design and the variable stiffness is often studied aiming to increase material functionality. There are many constitutive models for nonlinear elastic solids that are often used in design. These constitutive equations have been studied thoroughly under a typical deformation, such as uniaxial stretch and simple shear independently. However, a common deformation in MRE's is a combination of an imposed uniaxial stretch when a magnetic field is induced and external simple shear deformation. We study these constitutive models under an initial uniaxial stretch (pre-strain) and imposed simple shear to understand the effect on shear stiffness (Chapter 4). Surprisingly, we find that many of the constitutive models that were studied shear soften in compression. Some models eventually shear stiffen in compression when a finite elastic strain is achieved. We find that these predictions align with bio-polymer networks and semi-flexible fibers that also shear soften in compression. However, these models do not capture the behavior of biological tissues that shear stiffen in compression. We provide insight to the constitutive model predictions by referencing single chain models and making a connection to the chain extension ratio. Throughout this thesis, we aim to make a contribution to knowledge through studies that support the `design with' nonlinear elastic solids and the `design of' nonlinear elastic solids specific to soft machine design.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Darling, Carolyn Nicole

Contributors dc:contributor

• Ewoldt, Randy H

• Rheology, Nonlinear elastic, constitutive equations, Material selection, Material design, Magnetorheological elastomers

Statement dc:rights

• Copyright 2020 Carolyn Darling

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108174

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108174