The influence of laminate stacking sequence on ballistic limit using a combined Experimental/FEM/Artificial Neural Networks (ANN) methodology

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dc.contributor.author Artero Guerrero, José Alfonso
dc.contributor.author Pernas Sánchez, Jesús
dc.contributor.author Martín Montal, J.
dc.contributor.author Varas Doval, David
dc.contributor.author López Puente, Jorge
dc.date.accessioned 2020-12-03T12:40:53Z
dc.date.available 2020-12-03T12:40:53Z
dc.date.issued 2018-01-01
dc.identifier.bibliographicCitation Artero-Guerrero, J. A., Pernas-Sánchez, J., Martín-Montal, J., Varas, D., & López-Puente, J. (2018). The influence of laminate stacking sequence on ballistic limit using a combined experimental/FEM/artificial neural networks (ANN) methodology. Composite Structures, 183, 299-308.
dc.identifier.issn 0263-8223
dc.identifier.uri http://hdl.handle.net/10016/31532
dc.description.abstract Composite laminates subjected to high velocity impacts are usually studied by means of experimental or numerical approaches. Nevertheless, these techniques are not appropriate to analyze the wide range of possibilities in the design of laminates (a great amount of time and economic resources are required); therefore, more efficient methods would be desirable. This work presents the capability of an ANN approach to predict the change of the ballistic limit with the laminate stacking sequence, and hence to find the optimum laminate combination. In order to obtain a refined ANN tool, a combined methodology of experimental and finite element method has been used. The results of the experimentally validated FEM model, are used to provide the data to the ANN. Once trained, the ANN is able to predict accurately the ballistic limit of composite laminates studied. The ANN allows studying very efficiently the whole possibilities of laminate stacking sequence using the common orientations, in symmetric 12 plies laminates (4096 cases). In addition, a deeper comprehension of composite plates when subjected to high velocity impact has been achieved by means of the analysis of the results. Conclusions obtained can be used by composite design engineers to improve ballistic performance of composite plates. (C) 2017 Elsevier Ltd. All rights reserved.
dc.description.sponsorship This research was done with the financial support of the Spanish Ministry of Economy and Competitiveness under Project reference DPI2013-41094-R, and the Vicerrectorado de Política Científica UC3M (Projects 2014/00006/002 and 2013/00413/002) and also under the agreement between Universidad Carlos III de Madrid and Universitat de Barcelona PQ/2/2015–2016 and CQ/11/2015–2016.
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2017 Elsevier
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Finite element method
dc.subject.other Experimental tests
dc.subject.other Artificial neural network
dc.subject.other Multilayer perceptron
dc.subject.other High velocity impacts
dc.subject.other Composite laminates
dc.title The influence of laminate stacking sequence on ballistic limit using a combined Experimental/FEM/Artificial Neural Networks (ANN) methodology
dc.type article
dc.subject.eciencia Ingeniería Mecánica
dc.identifier.doi https://doi.org/10.1016/j.compstruct.2017.03.068
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. DPI2013-41094-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 299
dc.identifier.publicationlastpage 308
dc.identifier.publicationtitle Composite Structures
dc.identifier.publicationvolume 183
dc.identifier.uxxi AR/0000020649
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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