Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms
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Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SAGE Publications Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Artificial Neural Networks, Crashworthiness Optimization, Functionally Graded Thickness, Genetic Algorithms, Thin-Walled Tubes
Kaynak
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
231
Sayı
11
Künye
Baykasoğlu, 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.
