A one-dimensional phenomenological model for the two-way shape-memory effect in semi-crystalline networks. (5th December 2018)

• Record Type: Journal Article
• Authors: Scalet, Giulia; Pandini, Stefano; Messori, Massimo; Toselli, Maurizio; Auricchio, Ferdinando
• Is Part Of: Polymer. Volume 158(2018)
• Page Start: 130
• Page End: 148
• Publication Date: 2018-12-05
• Subjects: shape-memory polymers -- Two-way shape-memory effect -- Constitutive modeling -- Semi-crystalline networks; Polymers -- Periodicals; Polymerization -- Periodicals; Polymères -- Périodiques; Polymérisation -- Périodiques; 547.7
• DOI: 10.1016/j.polymer.2018.10.027
• Languages: English
• ISSNs: 0032-3861

Abstract

Abstract: The shape-memory effect (SME) in polymers is a stimuli-responsive feature that is attracting more and more attention, thanks to its usage in fields ranging from the biomedical sector to soft robotics. The two-way SME, in particular, has great potential for applications needing repeated cycling, e.g., actuators, and motivates the development of theoretical and numerical tools to support their design. The present paper aims to propose a one-dimensional finite-strain phenomenological model to describe the one-way SME as well as the two-way SME under stress conditions in thermo-responsive semi-crystalline crosslinked polymers. The model is simple, easy to implement, and based on parameters with a clear and direct physical interpretation and measurability. Model predictions are validated on experiments on semi-crystalline networks based on poly( ε -caprolactone) and demonstrate model ability in describing several material features as the effect of the crosslink density on SME, the dependence of microstructural evolution on applied load and heating/cooling rate, and the presence of thermal strains. Graphical abstract: Highlights: A 1D finite-strain phenomenological model for semi-crystalline networks is proposed. The model is able to describe both one-way and two-way shape-memory effect. Several features of material behavior are taken into account. The model is easy to implement and based on parameters with a clear physical interpretation. Compared to experimental data, numerical results show an overall good agreement.