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Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold
Published on: October 23, 2015
Modeling the relaxation mechanisms of amorphous shape memory polymers
Thao D Nguyen1, Christopher M Yakacki, Parth D Brahmbhatt
1Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA. vicky.nguyen@jhu.edu
Advanced Materials (Deerfield Beach, Fla.)
|July 30, 2010
Summary
This study enhances shape memory polymer modeling by extending a thermoviscoelastic model to capture time-dependent relaxation. The improved model accurately predicts temperature-dependent strain recovery in (meth)acrylate networks.
Area of Science:
- Polymer Science
- Materials Science
- Mechanical Engineering
Background:
- Constitutive modeling of polymers is crucial for understanding material behavior.
- Thermally activated shape memory polymers exhibit complex time-dependent responses.
- Existing models often simplify or overlook crucial relaxation mechanisms.
Purpose of the Study:
- To review and advance constitutive models for shape memory polymers.
- To present an extended thermoviscoelastic model incorporating multiple relaxation processes.
- To develop a method for parameter determination from experimental data.
Main Methods:
- Review of existing constitutive models for shape memory polymers.
- Development of an extended thermoviscoelastic model for amorphous networks.
- Implementation of a procedure for parameter extraction using thermomechanical experiments.
- Simulation of unconstrained recovery response in (meth)acrylate-based networks.
Main Results:
- The extended thermoviscoelastic model accurately describes time-dependent behavior by including multiple relaxation processes.
- A practical procedure for determining model parameters from experiments was established.
- Simulations demonstrated significant improvement in predicting temperature-dependent strain recovery.
Conclusions:
- The enhanced thermoviscoelastic model provides a more accurate representation of shape memory polymer behavior.
- The developed parameter determination method facilitates practical application of the model.
- This work advances the predictive capability for shape memory polymer networks.
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