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Titel: Neural Networks for Structural Optimisation of Mechanical Metamaterials
VerfasserIn: Bronder, Stefan
Diebels, Stefan
Jung, Anne
Sprache: Englisch
Titel: Proceedings in Applied Mathematics & Mechanics (PAMM)
Verlag/Plattform: Wiley
Erscheinungsjahr: 2020
Titel der Konferenz: 91st Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)
DDC-Sachgruppe: 500 Naturwissenschaften
600 Technik
Dokumenttyp: Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag)
Abstract: Mechanical metamaterials are man‐made designer materials with unusual properties, which are derived from the micro‐structure rather than the base material. Thus, metamaterials are suitable for tailoring and structural optimisation to enhance certain properties. A widely known example for this class of materials are auxetics with a negative Poisson's ratio. In this work an auxetic unit cell is modified with an additional half strut.During the deformation this half strut will get into contact with the unit cell and provide additional stability. This leads to a higher plateau stress and consequently to a higher energy absorption capacity. To achieve the maximum energy absorption capacity, a structural optimisation is carried out. But an optimisation exclusively based on finite element simulations is computationally costly and takes a lot of time. Therefore, in this contribution neural networks are used as a tool to speed up the optimisation. Neural networks are one of many machine learning methods and are able to approximate any arbitrary function on a highly abstract level. So the stress‐strain behaviour and its dependency from the geometry parameters of a type of microstructure can be learned by the neural network with only a few finite element simulations of varying geometry parameters. The modified auxetic structure is optimised with respect to the mass specific energy absorption capacity. As a result a qualitative trend for the optimal geometry parameters is obtained. However, the Poisson's ratio for this optimisation is close to zero.
DOI der Erstveröffentlichung: 10.1002/pamm.202000238
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-334338
hdl:20.500.11880/30744
http://dx.doi.org/10.22028/D291-33433
ISSN: 1617-7061
1617-7061
Datum des Eintrags: 26-Feb-2021
Bemerkung/Hinweis: Proc. Appl. Math. Mech., 20: e202000238
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Materialwissenschaft und Werkstofftechnik
Professur: NT - Prof. Dr. Stefan Diebels
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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