Driving Flip Origami Motions with Thermal-Responsive Shape Memory Alloy

Zhang, Yan; Ren, Hongliang

doi:10.1109/robio49542.2019.8961539

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…According to Liu et al (2012), self-folding is defined as a process of assembly that exclusively determines the path to fold the 2D pattern into a target 3D geometry. Many types of active materials have been investigated and applied to realize origami-inspired selffolding structures, including dielectric elastomers (DE) (Ahmed et al, 2014(Ahmed et al, , 2017aCarpi et al, 2011), electroactive polymers (EAPs), magnetoactive elastomers (MAEs), shape memory alloys (SMAs) (Hernandez et al, 2017;Jovanova et al, 2019;Katsumata et al, 2017;Zhang and Ren, 2019), shape memory polymers (SMPs) responsive to thermal field (Chen et al, 2019;Li et al, 2014;Yang et al, 2016), and SMPs triggered by light (Cehula and Pru˚sˇa, 2020;Laflin et al, 2012;Liu et al, 2014). Given that each active material exhibits its own advantages and disadvantages, the most appropriate active material for a particular application depends on the specific needs of the application.…”

Section: Introductionmentioning

confidence: 99%

A two-stage design optimization framework for multifield origami-inspired structures

Wei

Ahmed

Hong

et al. 2021

Journal of Intelligent Material Systems and Structures

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Different types of active materials have been used to actuate origami-inspired self-folding structures. To model the highly nonlinear deformation and material responses, as well as the coupled field equations and boundary conditions of such structures, high-fidelity models such as finite element (FE) models are needed but usually computationally expensive, which makes optimization intractable. In this paper, a computationally efficient two-stage optimization framework is developed as a systematic method for the multi-objective designs of such multifield self-folding structures where the deformations are concentrated in crease-like areas, active and passive materials are assumed to behave linearly, and low- and high-fidelity models of the structures can be developed. In Stage 1, low-fidelity models are used to determine the topology of the structure. At the end of Stage 1, a distance measure [Formula: see text] is applied as the metric to determine the best design, which then serves as the baseline design in Stage 2. In Stage 2, designs are further optimized from the baseline design with greatly reduced computing time compared to a full FEA-based topology optimization. The design framework is first described in a general formulation. To demonstrate its efficacy, this framework is implemented in two case studies, namely, a three-finger soft gripper actuated using a PVDF-based terpolymer, and a 3D multifield example actuated using both the terpolymer and a magneto-active elastomer, where the key steps are elaborated in detail, including the variable filter, metrics to select the best design, determination of design domains, and material conversion methods from low- to high-fidelity models. In this paper, analytical models and rigid body dynamic models are developed as the low-fidelity models for the terpolymer- and MAE-based actuations, respectively, and the FE model of the MAE-based actuation is generalized from previous work. Additional generalizable techniques to further reduce the computational cost are elaborated. As a result, designs with better overall performance than the baseline design were achieved at the end of Stage 2 with computing times of 15 days for the gripper and 9 days for the multifield example, which would rather be over 3 and 2 months for full FEA-based optimizations, respectively. Tradeoffs between the competing design objectives were achieved. In both case studies, the efficacy and computational efficiency of the two-stage optimization framework are successfully demonstrated.

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