Minimum-weight design for three dimensional woven composite stiffened panels using neural networks and genetic algorithms

Minimum-weight design for three dimensional woven composite stiffened panels using neural... The paper describes a modeling strategy for multi-scale analysis and optimization of stiffened panels, made of three-dimensional woven composites. Artificial neural network techniques are utilized to generate an approximate response for the optimum structural design in order to increase efficiency and applicability. The artificial neural networks are integrated with genetic algorithms to optimize mixed discrete–continuous design variables for the three dimensional woven composite structures. The proposed procedure is then applied to the multi-objective optimal design of a stiffened panel subject to buckling and post-buckling requirements. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Composite Structures Elsevier

Minimum-weight design for three dimensional woven composite stiffened panels using neural networks and genetic algorithms

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Publisher
Elsevier
Copyright
Copyright © 2015 Elsevier Ltd
ISSN
0263-8223
eISSN
1879-1085
D.O.I.
10.1016/j.compstruct.2015.08.077
Publisher site
See Article on Publisher Site

Abstract

The paper describes a modeling strategy for multi-scale analysis and optimization of stiffened panels, made of three-dimensional woven composites. Artificial neural network techniques are utilized to generate an approximate response for the optimum structural design in order to increase efficiency and applicability. The artificial neural networks are integrated with genetic algorithms to optimize mixed discrete–continuous design variables for the three dimensional woven composite structures. The proposed procedure is then applied to the multi-objective optimal design of a stiffened panel subject to buckling and post-buckling requirements.

Journal

Composite StructuresElsevier

Published: Dec 15, 2015

References

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