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Design optimisation of braided composite beams for lightweight rail structures using machine learning methods
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Singh, Anubhav, Gu, Zewen, Hou, Xiaonan, Liu, Yiding and Hughes, Darren J. (2022) Design optimisation of braided composite beams for lightweight rail structures using machine learning methods. Composite Structures, 282 . 115107. doi:10.1016/j.compstruct.2021.115107 ISSN 0263-8223.
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Official URL: http://dx.doi.org/10.1016/j.compstruct.2021.115107
Abstract
Braided composites have seen substantial industrial uptake for structural applications in the past decade. The dependence of their properties on braid angle provides opportunities for lightweighting through structure-specific optimisation. This paper presents an integrated approach, combining finite element (FE) simulations and a genetic algorithm (GA) to optimise braided beam structures in the spaceframe chassis of a rail vehicle. The braid angle and number of layers for each beam were considered as design variables. A set of 200 combinations of these variables were identified using a sampling strategy for FE simulations. The results were utilised to develop a surrogate model using genetic programming (GP) to correlate the design variables with structural mass and FE-predicted chassis displacements under standard loads. The surrogate model was then used to optimise the design variables using GA to minimise mass without compromising mechanical performance. The optimised design rendered approximately 15.7% weight saving compared to benchmark design.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Journal or Publication Title: | Composite Structures | ||||||||
Publisher: | Elsevier Ltd | ||||||||
ISSN: | 0263-8223 | ||||||||
Official Date: | 15 February 2022 | ||||||||
Dates: |
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Volume: | 282 | ||||||||
Article Number: | 115107 | ||||||||
DOI: | 10.1016/j.compstruct.2021.115107 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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