Hierarchical design of morphing shape-memory polymer structures via topology optimization

Shape memory polymers (SMPs) are a class of active polymeric materials that have the ability to regain their original undeformed configuration from a deformed state under the application of an external stimulus. In this study, we focused on the design optimization of SMPs that are actuated by the application of a thermal gradient. This study puts forward an optimization framework for systematic design of shape morphing structures capable of exhibiting large deformations with SMPs. A hierarchical two-stage design procedure is adopted. In the first stage, basic unit structures including a twisting structure and a bending structure are designed using a topology optimization framework. A finite-element analysis incorporating the additive decomposition of small strains, is implemented to analyze and predict temperature-dependent displacement response of SMPs. The finite element method consists of a viscoelastic material model combined with a temperature-dependent strain storage mechanism, giving SMPs their characteristic property. The thermo-mechanical characteristics of SMPs are exploited to actuate structural deflection to enable morphing toward a target shape. A time-dependent adjoint sensitivity formulation implemented through a recursive algorithm is used to calculate the gradients required for the topology optimization algorithm. Multimaterial topology optimization combined with the thermo-mechanical programming cycle is used to optimally distribute the active and passive SMP materials within the design domain. This allows us to tailor the response of the structures to design them with specific target displacements, by exploiting the difference in the glass-transition temperatures of the two SMP materials. Forward analysis and sensitivity calculations are combined in an PETSc-based optimization framework to enable efficient multi-functional, multimaterial structural design with controlled deformations. In stage II of the design process, the basic twisting and bending structures are fabricated and their kinematic characteristics are validated using a 4D printing technique. The main goal of this design stage is to use these basic twisting and bending structures to computationally design a self-tying knot. A forward kinematics framework is implemented using these bending and twisting angles as input and the optimal sequence of the basic twisting and bending structures along a morphable kinematic chain are generated by using a genetic algorithm with a predetermined ideal knot chosen as the target shape. The optimal sequence is then 3D printed and subjected to the thermo-mechanical programming cycle to compare the kinematic characteristics of the computationally designed structure with the mathematical knot. The results obtained show good agreement between the ideal knot and the computational design.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Aerospace Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Bhattacharyya, Anurag

Contributors dc:contributor

• James, Kai A.
• Tortorelli, Daniel A.
• Lambros, John
• Zhang, X. Shelly

• Topology Optimization, Shape Memory Polymers, Adjoint Sensitivity, 4D Printing

Statement dc:rights

• Copyright 2021 Anurag Bhattacharyya

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/113319

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/113319