Rights statement: This is the author’s version of a work that was accepted for publication in Composite Strutures. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Composite Structures, 282, 2022 DOI: 10.1016/j.compstruct.2021.115107
Accepted author manuscript, 1.93 MB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Design optimisation of braided composite beams for lightweight rail structures using machine learning methods
AU - Singh, A.
AU - Gu, Z.
AU - Hou, X.
AU - Liu, Y.
AU - Hughes, D.J.
N1 - This is the author’s version of a work that was accepted for publication in Composite Strutures. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Composite Structures, 282, 2022 DOI: 10.1016/j.compstruct.2021.115107
PY - 2022/2/15
Y1 - 2022/2/15
N2 - 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.
AB - 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.
KW - Braided composites
KW - Design optimisation
KW - Finite element analysis
KW - Genetic algorithm
KW - Genetic programming
KW - Lightweighting
KW - Benchmarking
KW - Chassis
KW - Composite materials
KW - Finite element method
KW - Machine learning
KW - Structural optimization
KW - Composite beam
KW - Design optimization
KW - Design variables
KW - Finite element analyse
KW - Finite elements simulation
KW - Machine learning methods
KW - Structural applications
KW - Surrogate modeling
KW - Genetic algorithms
U2 - 10.1016/j.compstruct.2021.115107
DO - 10.1016/j.compstruct.2021.115107
M3 - Journal article
VL - 282
JO - Composite Structures
JF - Composite Structures
SN - 0263-8223
M1 - 115107
ER -