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      Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams

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          Abstract

          This research is taking the first steps toward applying a 2D dragonfly wing skeleton in the design of an airplane wing using artificial intelligence. The work relates the 2D morphology of the structural network of dragonfly veins to a secondary graph that is topologically dual and geometrically perpendicular to the initial network. This secondary network is referred as the reciprocal diagram proposed by Maxwell that can represent the static equilibrium of forces in the initial graph. Surprisingly, the secondary graph shows a direct relationship between the thickness of the structural members of a dragonfly wing and their in‐plane static equilibrium of forces that gives the location of the primary and secondary veins in the network. The initial and the reciprocal graph of the wing are used to train an integrated and comprehensive machine‐learning model that can generate similar graphs with both primary and secondary veins for a given boundary geometry. The result shows that the proposed algorithm can generate similar vein networks for an arbitrary boundary geometry with no prior topological information or the primary veins' location. The structural performance of the dragonfly wing in nature also motivated the authors to test this research's real‐world application for designing the cellular structures for the core of airplane wings as cantilever porous beams. The boundary geometry of various airplane wings is used as an input for the design proccedure. The internal structure is generated using the training model of the dragonfly veins and their reciprocal graphs. One application of this method is experimentally and numerically examined for designing the cellular core, 3D printed by fused deposition modeling, of the airfoil wing; the results suggest up to 25% improvements in the out‐of‐plane stiffness. The findings demonstrate that the proposed machine‐learning‐assisted approach can facilitate the generation of multiscale architectural patterns inspired by nature to form lightweight load‐bearable elements with superior structural properties.

          Abstract

          This research investigates the use of graphic statics and machine learning to analyze the structural geometry of a dragonfly wing, understand its performance, and generate similar patterns. It can be used in a more universal workflow to generate structural forms inspired from the collected dataset of a wider range of species.

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          Most cited references14

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          Die graphische Statik

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            Graphical Statics: Two Treatises on the Graphical Calculus and Reciprocal Figures in Graphical Statics…

            L Cremona (1890)
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              A Practical Introduction to Computer Vision with OpenCV

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                Author and article information

                Contributors
                hamid.akbarzadeh@mcgill.ca
                masouda@design.upenn.edu
                Journal
                Adv Sci (Weinh)
                Adv Sci (Weinh)
                10.1002/(ISSN)2198-3844
                ADVS
                Advanced Science
                John Wiley and Sons Inc. (Hoboken )
                2198-3844
                29 April 2023
                June 2023
                : 10
                : 18 ( doiID: 10.1002/advs.v10.18 )
                : 2207635
                Affiliations
                [ 1 ] Polyhedral Structures Laboratory, Department of Architecture, Weitzman School of Design University of Pennsylvania Philadelphia PA 19146 USA
                [ 2 ] General Office, Department of Architecture and Civil Engineering City University of Hong Kong 83 Tat Chee Avenue, Kowloon Tong Kowloon HKSAR China
                [ 3 ] Advanced Multifunctional and Multiphysics Metamaterials Lab (AM3L), Department of Bioresource Engineering McGill University Montreal QC H9X 3V9 Canada
                [ 4 ] Mathematical Institute Leiden University Leiden 2333CA The Netherlands
                [ 5 ] Department of Mechanical Engineering McGill University Montreal QC H3A 0C3 Canada
                [ 6 ] General Robotic, Automation, Sensing and Perception (GRASP) Lab, School of Engineering and Applied Science University of Pennsylvania 3330 Walnut St Philadelphia PA 19104 USA
                Author notes
                Author information
                https://orcid.org/0000-0001-7549-9713
                https://orcid.org/0000-0003-0097-0341
                https://orcid.org/0000-0002-6402-615X
                Article
                ADVS5407
                10.1002/advs.202207635
                10288228
                37119466
                5e5b233c-87eb-4c98-b7a0-2c9670246945
                © 2023 The Authors. Advanced Science published by Wiley‐VCH GmbH

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 February 2023
                : 24 December 2022
                Page count
                Figures: 8, Tables: 1, Pages: 15, Words: 10560
                Funding
                Funded by: FRQNT , doi 10.13039/501100003151;
                Award ID: FRQNTDoctoralResearchScholarship(B2X)
                Funded by: McGill University , doi 10.13039/100008582;
                Funded by: US National Science Foundation (NSF), Directorate for Engineering
                Award ID: NSF FMRG‐CMMI 2037097
                Award ID: NSF CAREER‐CMMI 1944691
                Funded by: Natural Sciences and Engineering Research Council of Canada , doi 10.13039/501100000038;
                Award ID: RGPIN‐2022‐04493
                Funded by: Canada Research Chairs , doi 10.13039/501100001804;
                Award ID: CRCTier2inMultifunctionalMetamaterials
                Funded by: Canada Foundation for Innovation (CFI)
                Award ID: JohnR.EvansLeadersFund
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                June 23, 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:23.06.2023

                3d printing,bio‐inspired structures,form and force diagrams,graphic statics,machine learning,structural form‐finding

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