Enhanced multimaterial 4D printing with active hinges

• Published in: Smart materials and structures Vol. 27; no. 6; pp. 65027 - 65041
• Main Authors: Akbari, Saeed, Sakhaei, Amir Hosein, Kowsari, Kavin, Yang, Bill, Serjouei, Ahmad, Yuanfang, Zhang, Ge, Qi
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.06.2018
• Subjects: 4D printing; enhanced recovery; finite element modeling; shape memory polymer
• DOI: 10.1088/1361-665X/aabe63
• ISSN: 0964-1726

Despite great progress in four-dimensional (4D) printing, i.e. three-dimensional (3D) printing of active (stimuli-responsive) materials, the relatively low actuation force of the 4D printed structures often impedes their engineering applications. In this study, we use multimaterial inkjet 3D printing technology to fabricate shape memory structures, including a morphing wing flap and a deployable structure, which consist of active and flexible hinges joining rigid (non-active) parts. The active hinges, printed from a shape memory polymer (SMP), lock the structure into a second temporary shape during a thermomechanical programming process, while the flexible hinges, printed from an elastomer, effectively increase the actuation force and the load-bearing capacity of the printed structure as reflected in the recovery ratio. A broad range of mechanical properties such as modulus and failure strain can be achieved for both active and flexible hinges by varying the composition of the two base materials, i.e. the SMP and the elastomer, to accommodate large deformation induced during programming step, and enhance the recovery in the actuating step. To find the important design parameters, including local deformation, shape fixity and recovery ratio, we conduct high fidelity finite element simulations, which are able to accurately predict the nonlinear deformation of the printed structures. In addition, a coupled thermal-electrical finite element analysis was performed to model the heat transfer within the active hinges during the localized Joule heating process. The model predictions showed good agreement with the measured temperature data and were used to find the major parameters affecting temperature distribution including the applied voltage and the convection rate.