Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , |
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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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 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
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http://hdl.handle.net/10183/205035 |
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1679-7825 |
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000825932 |
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http://hdl.handle.net/10183/205035 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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