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research-article

Discovering Sequenced Origami Folding Through Nonlinear Mechanics and Topology Optimization

[+] Author and Article Information
Andrew/S Gillman

UES, Inc., Beavercreek, OH; Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH
andrew.gillman.1.ctr@us.af.mil

Kazuko Fuchi

University of Dayton Research Institute, Dayton, OH; Aerospace Systems Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH
kfuchi1@udayton.edu

Philip R. Buskohl

Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH
philip.buskohl.1@us.af.mil

1Corresponding author.

ASME doi:10.1115/1.4041782 History: Received January 31, 2018; Revised October 06, 2018

Abstract

Origami folding provides a novel method to transform two dimensional sheets into complex functional structures. However,the enormity of the foldable design space necessitates development of algorithms to efficiently discover new origami fold patterns with specific performance objectives. To address this challenge, this work combines a recently developed efficient modified truss finite element model with a ground structure-based topology optimization framework. A nonlinear mechanics model is required to model the sequenced motion and large folding common in the actuation of origami structures. These highly nonlinear motions limit the ability to define convex objective functions, and parallelizable evolutionary optimization algorithms for traversing non-convex origami design problems are developed and considered. The ability of this framework to discover fold topologies that maximize targeted actuation is verified for the well known "Chomper" and "Square Twist" patterns. The role of critical points and bifurcations emanating from sequenced deformation mechanisms (including interplay of folding, facet bending and stretching) on design optimization is analyzed. In addition, the performance of both gradient and evolutionary optimization algorithms are explored, and genetic algorithms consistently yield solutions with better performance given the apparent non-convexity of the response-design space.

Copyright (c) 2018 by ASME
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