Design optimization of laminated composite structures using artificial neural network and genetic algorithm

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dc.contributor.authorLiu, Xiaoyang
dc.contributor.authorQin, Jian
dc.contributor.authorZhao, Kai
dc.contributor.authorFeatherson, Carol A.
dc.contributor.authorKennedy, David
dc.contributor.authorJing, Yucai
dc.contributor.authorYang, Guotao
dc.date.accessioned2022-12-12T10:00:14Z
dc.date.available2022-12-12T10:00:14Z
dc.date.issued2022-11-26
dc.description.abstractIn this paper, an efficient method for performing minimum weight optimization of composite laminates using artificial neural network (ANN) based surrogate models is proposed. By predicting the buckling loads of the laminates using ANN the use of time-consuming buckling evaluations during the iterative optimization process are avoided. Using for the first time lamination parameters, laminate thickness and other dimensional parameters as inputs for these ANN models significantly reduces the number of models required and therefore computational cost of considering laminates with many different numbers of layers and total thickness. Besides, as the stacking sequences are represented by lamination parameters, the number of inputs of the ANN models is also significantly reduced, avoiding the curse of dimensionality. Finite element analysis (FEA) is employed together with the Latin hypercube sampling (LHS) method to generate the database for the training and testing of the ANN models. The trained ANN models are then employed within a genetic algorithm (GA) to optimize the stacking sequences and structural dimensions to minimize the weight of the composite laminates. The advantages of using ANN in predicting buckling load is proved by comparison with other machine learning methods, and the effectiveness and efficiency of the proposed optimization method is demonstrated through the optimization of flat, blade-stiffened and hat-stiffened laminates.en_UK
dc.identifier.citationLiu X, Qin J, Zhao K, et al., (2023) Design optimization of laminated composite structures using artificial neural network and genetic algorithm, Composite Structures, Volume 305, February 2023, Article number 116500en_UK
dc.identifier.issn0263-8223
dc.identifier.urihttps://doi.org/10.1016/j.compstruct.2022.116500
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18777
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectComposite laminatesen_UK
dc.subjectLamination parametersen_UK
dc.subjectOptimizationen_UK
dc.subjectArtificial neural networken_UK
dc.subjectGenetic algorithmen_UK
dc.titleDesign optimization of laminated composite structures using artificial neural network and genetic algorithmen_UK
dc.typeArticleen_UK

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