Many-objective design optimisation of a plain weave fabric composite
Many-objective design optimisation of a plain weave fabric composite
Plain weave fabrics provide low-cost composites used in many applications. Their mechanical properties are dependent on the weave and the yarn dimensions, which provides a complex design space to ensure optimal properties for a given application. Genetic Algorithms are commonly used in the literature to optimise the performance of composite materials but are currently limited to two or three objectives, where the optimisation may improve the specified properties but degrade others. In this paper 9 top performing Genetic Algorithms are benchmarked to find designs that respectively satisfy five-objective, three-objective and bi-objective formulations. The results show that the consideration of the five-objective problem is important, since the designs for the five-objective formulation give a wider range of results. These results do not include designs from the optimisation with the more limited objectives, meaning that these designs would need to be redesigned to be practical and demonstrating the benefits of optimisation with more objectives. cMLSGA is shown to be the strongest solver for these problems, contradicting the findings from the Evolutionary Computation literature. When compared with a current weave pattern, the five-objective optimisation provides 101 designs which improve all 5 material properties, with up to 76.61% improvements on the four mechanical properties and a maximum 37.73% reduction on areal density; there are weave patterns with designs that are specific to each of the properties individually.
Genetic Algorithms, Many-objective optimisation, Plain weave fabric (PWF), Shear properties, Tensile properties
Wang, Zhenzhou
794c41fe-f5da-4da4-8f1c-c7beb06f87eb
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
1 April 2022
Wang, Zhenzhou
794c41fe-f5da-4da4-8f1c-c7beb06f87eb
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Wang, Zhenzhou and Sobey, Adam
(2022)
Many-objective design optimisation of a plain weave fabric composite.
Composite Structures, 285, [115246].
(doi:10.1016/j.compstruct.2022.115246).
Abstract
Plain weave fabrics provide low-cost composites used in many applications. Their mechanical properties are dependent on the weave and the yarn dimensions, which provides a complex design space to ensure optimal properties for a given application. Genetic Algorithms are commonly used in the literature to optimise the performance of composite materials but are currently limited to two or three objectives, where the optimisation may improve the specified properties but degrade others. In this paper 9 top performing Genetic Algorithms are benchmarked to find designs that respectively satisfy five-objective, three-objective and bi-objective formulations. The results show that the consideration of the five-objective problem is important, since the designs for the five-objective formulation give a wider range of results. These results do not include designs from the optimisation with the more limited objectives, meaning that these designs would need to be redesigned to be practical and demonstrating the benefits of optimisation with more objectives. cMLSGA is shown to be the strongest solver for these problems, contradicting the findings from the Evolutionary Computation literature. When compared with a current weave pattern, the five-objective optimisation provides 101 designs which improve all 5 material properties, with up to 76.61% improvements on the four mechanical properties and a maximum 37.73% reduction on areal density; there are weave patterns with designs that are specific to each of the properties individually.
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Many-objective design optimisation of a plain weave fabric composite _FINAL Submission
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Accepted/In Press date: 9 January 2022
e-pub ahead of print date: 12 January 2022
Published date: 1 April 2022
Additional Information:
Funding Information:
This project was supported by the Lloyd's Register Foundation and China Scholarship Council.
Keywords:
Genetic Algorithms, Many-objective optimisation, Plain weave fabric (PWF), Shear properties, Tensile properties
Identifiers
Local EPrints ID: 454323
URI: http://eprints.soton.ac.uk/id/eprint/454323
ISSN: 0263-8223
PURE UUID: b5dea403-858e-4449-9451-b48322ce4625
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Date deposited: 07 Feb 2022 17:42
Last modified: 17 Mar 2024 07:03
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Author:
Zhenzhou Wang
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