Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms

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dc.authorid0000-0001-7583-7655
dc.contributor.authorBaykasoğlu, Adil
dc.contributor.authorBaykasoğlu, Cengiz
dc.date.accessioned2019-05-13T08:58:20Z
dc.date.available2019-05-13T08:58:20Z
dc.date.issued2017
dc.departmentHitit Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractThe objective of this paper is to develop a multiple objective optimization procedure for crashworthiness optimization of circular tubes having functionally graded thickness. The proposed optimization approach is based on finite element analyses for construction of sample design space and verification; artificial neural networks for predicting objective functions values (peak crash force and specific energy absorption) for design parameters; and genetic algorithms for generating design parameters alternatives and determining optimal combination of them. The proposed approach seaminglesly integrates artificial neural networks and genetic algorithms. Artificial neural network acts as an objective function evaluator within the multiple objective genetic algorithms. We have shown that the proposed approach is able to generate Pareto optimal designs which are in a very good agreement with the finite element results. © Institution of Mechanical Engineers.
dc.identifier.citationBaykasoğlu, A., Baykasoğlu, C. (2017). Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 231(11), 2005-2016.
dc.identifier.doi10.1177/0954406215627181
dc.identifier.endpage2016en_US
dc.identifier.issn0954-4062
dc.identifier.issue11en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage2005en_US
dc.identifier.urihttps://doi.org/10.1177/0954406215627181
dc.identifier.urihttps://hdl.handle.net/11491/1116
dc.identifier.volume231en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSAGE Publications Ltd
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Neural Networksen_US
dc.subjectCrashworthiness Optimizationen_US
dc.subjectFunctionally Graded Thicknessen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectThin-Walled Tubesen_US
dc.titleMultiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms
dc.typeArticle

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