Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements

Detalhes bibliográficos
Autor(a) principal: Cardozo López, Sergio Daniel
Data de Publicação: 2011
Outros Autores: Gomes, Herbert Martins, Awruch, Armando Miguel
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/205035
Resumo: Structural optimization using computational tools has become a major research field in recent years. Methods commonly used in structural analysis and optimization may demand considerable computational cost, depending on the problem complexity. Therefore, many techniques have been evaluated in order to diminish such impact. Among these various techniques, Artificial Neural Networks (ANN) may be considered as one of the main alternatives, when combined with classic analysis and optimization methods, to reduce the computational effort without affecting the final solution quality. Use of laminated composite structures has been continuously growing in the last decades due to the excellent mechanical properties and low weight characterizing these materials. Taken into account the increasing scientific effort in the different topics of this area, the aim of the present work is the formulation and implementation of a computational code to optimize manufactured complex laminated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimization and ANN to approximate the finite element solutions. The modules for linear and geometrically non-linear static finite element analysis and for optimize laminated composite plates and shells, using GA, were previously implemented. Here, the finite element module is extended to analyze dynamic responses to solve optimization problems based in frequencies and modal criteria, and a perceptron ANN module is added to approximate finite element analyses. Several examples are presented to show the effectiveness of ANN to approximate solutions obtained using the FEM and to reduce significatively the computational cost.
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spelling Cardozo López, Sergio DanielGomes, Herbert MartinsAwruch, Armando Miguel2020-01-29T04:08:33Z20111679-7825http://hdl.handle.net/10183/205035000825932Structural optimization using computational tools has become a major research field in recent years. Methods commonly used in structural analysis and optimization may demand considerable computational cost, depending on the problem complexity. Therefore, many techniques have been evaluated in order to diminish such impact. Among these various techniques, Artificial Neural Networks (ANN) may be considered as one of the main alternatives, when combined with classic analysis and optimization methods, to reduce the computational effort without affecting the final solution quality. Use of laminated composite structures has been continuously growing in the last decades due to the excellent mechanical properties and low weight characterizing these materials. Taken into account the increasing scientific effort in the different topics of this area, the aim of the present work is the formulation and implementation of a computational code to optimize manufactured complex laminated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimization and ANN to approximate the finite element solutions. The modules for linear and geometrically non-linear static finite element analysis and for optimize laminated composite plates and shells, using GA, were previously implemented. Here, the finite element module is extended to analyze dynamic responses to solve optimization problems based in frequencies and modal criteria, and a perceptron ANN module is added to approximate finite element analyses. Several examples are presented to show the effectiveness of ANN to approximate solutions obtained using the FEM and to reduce significatively the computational cost.application/pdfengLatin american journal of solids and structures [recurso eletrônico]. Rio de Janeiro, RJ. Vol. 8, n0. 4 (Dec. 2011), p. 413-427Algoritmos genéticosRedes neurais artificiaisElementos finitosCompósitosLaminated composite plates and shellsArtificial neural networksOptimizationGenetic algorithmsFinite elementOptimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elementsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT000825932.pdf.txt000825932.pdf.txtExtracted Texttext/plain31196http://www.lume.ufrgs.br/bitstream/10183/205035/2/000825932.pdf.txt0380c8b14869845ecff082a4587e9886MD52ORIGINAL000825932.pdfTexto completo (inglês)application/pdf1104549http://www.lume.ufrgs.br/bitstream/10183/205035/1/000825932.pdfe054b65ff16401658f6b4ae1f6f96756MD5110183/2050352022-02-22 05:02:09.466288oai:www.lume.ufrgs.br:10183/205035Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2022-02-22T08:02:09Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
title Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
spellingShingle Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
Cardozo López, Sergio Daniel
Algoritmos genéticos
Redes neurais artificiais
Elementos finitos
Compósitos
Laminated composite plates and shells
Artificial neural networks
Optimization
Genetic algorithms
Finite element
title_short Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
title_full Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
title_fullStr Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
title_full_unstemmed Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
title_sort Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
author Cardozo López, Sergio Daniel
author_facet Cardozo López, Sergio Daniel
Gomes, Herbert Martins
Awruch, Armando Miguel
author_role author
author2 Gomes, Herbert Martins
Awruch, Armando Miguel
author2_role author
author
dc.contributor.author.fl_str_mv Cardozo López, Sergio Daniel
Gomes, Herbert Martins
Awruch, Armando Miguel
dc.subject.por.fl_str_mv Algoritmos genéticos
Redes neurais artificiais
Elementos finitos
Compósitos
topic Algoritmos genéticos
Redes neurais artificiais
Elementos finitos
Compósitos
Laminated composite plates and shells
Artificial neural networks
Optimization
Genetic algorithms
Finite element
dc.subject.eng.fl_str_mv Laminated composite plates and shells
Artificial neural networks
Optimization
Genetic algorithms
Finite element
description Structural optimization using computational tools has become a major research field in recent years. Methods commonly used in structural analysis and optimization may demand considerable computational cost, depending on the problem complexity. Therefore, many techniques have been evaluated in order to diminish such impact. Among these various techniques, Artificial Neural Networks (ANN) may be considered as one of the main alternatives, when combined with classic analysis and optimization methods, to reduce the computational effort without affecting the final solution quality. Use of laminated composite structures has been continuously growing in the last decades due to the excellent mechanical properties and low weight characterizing these materials. Taken into account the increasing scientific effort in the different topics of this area, the aim of the present work is the formulation and implementation of a computational code to optimize manufactured complex laminated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimization and ANN to approximate the finite element solutions. The modules for linear and geometrically non-linear static finite element analysis and for optimize laminated composite plates and shells, using GA, were previously implemented. Here, the finite element module is extended to analyze dynamic responses to solve optimization problems based in frequencies and modal criteria, and a perceptron ANN module is added to approximate finite element analyses. Several examples are presented to show the effectiveness of ANN to approximate solutions obtained using the FEM and to reduce significatively the computational cost.
publishDate 2011
dc.date.issued.fl_str_mv 2011
dc.date.accessioned.fl_str_mv 2020-01-29T04:08:33Z
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10183/205035
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dc.relation.ispartof.pt_BR.fl_str_mv Latin american journal of solids and structures [recurso eletrônico]. Rio de Janeiro, RJ. Vol. 8, n0. 4 (Dec. 2011), p. 413-427
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