Thermomechanics of printed anisotropic shape memory elastomeric composites

Shape memory polymers (SMPs) are a class of active materials that have the capability of fixing a temporary shape and recovering to a permanent shape in response to environmental stimuli. While SMPs have been studied intensively, researchers paid comparatively fewer attentions to SMPs exhibiting anisotropic mechanical and shape memory behaviors by controlling the microscopic architectures. At the same time, the rapidly developed three-dimension (3D) printing technologies enable us to directly print complex 3D structures with multimaterials and provide possibilities to fabricate anisotropic shape memory polymer by precisely controlling the microstructure of fibers. In this paper, we present a new approach for fabricating printed anisotropic shape memory elastomeric composites (p-ASMECs) by taking advantage of 3D printing. In the fabrication process, an elastomer is first printed as matrix and the orientation of fibers is defined by the precisely printed channels. By interrupting the printing process, the printed channels are filled with the crystallizable polymeric fibers that endow the shape memory effect into the composite system. The p-ASMEC5 exhibit large, controllable anisotropy in both mechanical and shape memory behaviors that can be precisely controlled by geometric parameters of the microstructure such as fiber's volume fraction and position in space. To facilitate design of p-ASEMCs, we also developed a thermomechanical constitutive framework to describe the complex, anisotropic, larger deformation thermomechanical behaviors of p-ASEMCs. The developed constitutive model, used to explain the phenomenon that the lowest stiffness of p-ASEMCs occurs at the fiber orientation angle theta approximate to 55 degrees, successfully predicts the anisotropic shape memory behavior of p-ASMECs, guides the design to enhance the temporary shape fixity, and simulates the shear deformation of the temporary shape after completely releasing the external constraints. (C) 2016 Elsevier Ltd. All rights reserved.