Session: SYMP 2-4: Structure and Performance of Shape Memory Polymer Actuators

Paper Number: 140374

140374 - Impact of Yarn Geometry and Programming on Thermo-Mechanical Performance of Shape Memory Polymer Yarns 

Smart materials have the capacity to transform the way we interact with the world around us. Shape memory polymers (SMPs) enable textiles to respond to a stimulus such as heat by changing shape or stress. Incorporating SMPs into textile structures has the potential to improve the performance and functionality of compression garments. For example, instead of passively stretching the textile to get it on, which may be difficult or some users, the textile itself could start out loose and then tighten in response to heat. A building block for many textiles is yarn, which comes in a diversity of structures. Previous research indicated that increasing the amount of yarn twist increased the amount of strain in a stress programmed yarn without compromising the stress generated at the recovery step. Although SMPs have been incorporated into yarn and textile structures for force generation, understanding the impact of yarn geometry and shape memory programming method on thermo-mechanical performance will allow for improved smart textile designs. This research evaluates the effect of yarn geometry and shape programming parameters on the force generation of SMP yarns. The structure of interest for this research is yarn composed of a finite number of continuous filaments, or few-filament yarns. Commercial monofilaments of a variety of materials are evaluated by dynamic mechanical analysis to determine potential for shape memory behavior. Chemical morphology of candidate materials is also evaluated by differential scanning calorimetry. Monofilaments selected based off of storage modulus and transition temperature are then spun into yarns using a custom yarn spinning machine. These yarns have different geometric properties including turns per centimeter, balance, number of filaments, and ply. Semi-analytical modeling and a design of experiments guide the selection of geometric parameters for experimental investigation. The yarns are evaluated by using a dynamic mechanical analysis machine to expose the yarn to a shape memory programming and recovery cycle. The programming portion of the cycle can be either stress controlled or strain controlled. The method of interest for the recovery portion of the cycle is displacement-controlled testing. Displacement controlled testing reflects the potential application of a compression garment generating force on a rigid body. The results of the thermo-mechanical testing of the few-filament yarns are portrayed as a catalog indicating the effect of yarn geometry and shape memory cycle parameters on the shape memory performance. This catalog delineates the design space and enables informed yarn design when developing a smart textile composed of SMP yarns.

Presenting Author: Michaela Andrews University of Minnesota - Mechanical Engineering

Presenting Author Biography: Michaela is a PhD candidate at the University of Minnesota and is co-advised by Prof. Julianna Abel and Prof. Sue Mantell. She is a researcher in the Design of Active Materials and Structures Lab, and the Polymer Materials and Mechanics Laboratory. Her research focus is incorporating shape memory polymers into yarn and textile structures for medical compression applications.

Authors:

Michaela Andrews
Julianna Abel
Susan Mantell