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Design of thermally deformable laminates using machine learning

Abdel-Rahman, A; Kosicki, M; Michalatos, P; Tsigkari, M; (2019) Design of thermally deformable laminates using machine learning. In: Zingoni, A, (ed.) Advances in Engineering Materials, Structures and Systems: Innovations, Mechanics and Applications. (pp. pp. 1016-1021). CRC PRESS-BALKEMA Green open access

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Abstract

Recent advances in material science and manufacturing have enabled designers to create objects which respond to changing environmental conditions by controlled deformation, without external mechanical or electrical actuation. The design of such smart materials has mostly been done through trial and error due to their complex nonlinear behavior. This paper will present how this problem is addressed by introducing a novel inverse design workflow. In this, a desired structural deformation is used as an input to a machine learned model which subsequently outputs the required geometric and material properties that will produce said deformation when exposed to an external stimulus. This workflow uses a Generative Adversarial Neural Network (GANN) trained on pairs of input cut-out patterns of laminate layers and their nonlinear Finite Element Analysis (FEA) simulation results. The method offers a significant performance speed-up while maintaining acceptable levels of accuracy, especially at the early design stage. This methodology could be further extended to the design of any nonlinear mechanical deformation.

Type: Proceedings paper
Title: Design of thermally deformable laminates using machine learning
Event: 7th International Conference on Structural Engineering, Mechanics and Computation (SEMC)
Location: Cape Town, SOUTH AFRICA
Dates: 02 September 2019 - 04 September 2019
ISBN-13: 978-1-138-38696-9
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.routledge.com/Advances-in-Engineering-...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Architecture
URI: https://discovery.ucl.ac.uk/id/eprint/10114522
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