Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints

To ensure realistic results when modeling welded joints using the finite element method (FEM), it is essential to appropriately characterize the thermo-mechanical behavior of the elastic-plastic Finite Element (FE) models. This task is complex. Any small differences between the actual welded joints...

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Autores: Lostado Lorza, Rubén, Escribano García, Rubén, Fernández Martínez, Roberto, Martínez Calvo, María Ángeles
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/27777
Acceso en línea:http://hdl.handle.net/10810/27777
Access Level:acceso abierto
Palabra clave:finite element method
genetic algorithms
welding temperature distribution
angular distortion
multi-objective functions
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spelling Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded JointsLostado Lorza, RubénEscribano García, RubénFernández Martínez, RobertoMartínez Calvo, María Ángelesfinite element methodgenetic algorithmswelding temperature distributionangular distortionmulti-objective functionsTo ensure realistic results when modeling welded joints using the finite element method (FEM), it is essential to appropriately characterize the thermo-mechanical behavior of the elastic-plastic Finite Element (FE) models. This task is complex. Any small differences between the actual welded joints and the welded joints based on FEM can be amplified enormously in the presence of nonlinearities. Due to the intense concentration of heat on a small area to create such joints, the regions near the weld line undergo severe thermal cycles. These generate significant angular distortion due mainly to the residual stresses. This paper proposes a method to determine the parameters that are most appropriate for modeling the Butt joint single V-groove welded joint FE models' thermo-mechanical behavior that were created by the one-pass Gas Metal Arc Welding (GMAW). The method is based on experimental data, as well as genetic algorithms (GA) with multi-objective functions. As a practical example, the proposed methodology is validated with three different welded joints specimens that are manufactured by different voltages and currents (26 volts and 140 amps, 28 volts and 210 amps, and 35 volts and 260 amps). The electrode orientation, shielding gas flow rate, distance between nozzle and plate, and welding speed were considered to be constant for all of the specimens that were studied, and their values were 80deg, 20.0 L/min, 4.0 mm, and 6 mm/s, respectively. The base material was EN 235JR low carbon steel, whereas the weld bead was ER70S-6 for the three specimens that were welded. An agreement between the temperature field and the angular distortion that was obtained by the adjusted FE models and those that were obtained experimentally demonstrates that the proposed methodology may be valid for automatically determining the most appropriate parameters.MDPI201820182018info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/27777reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttp://www.mdpi.com/2075-4701/8/4/230info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Atribución 3.0 Españaoai:addi.ehu.eus:10810/277772026-06-18T09:23:17Z
dc.title.none.fl_str_mv Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints
title Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints
spellingShingle Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints
Lostado Lorza, Rubén
finite element method
genetic algorithms
welding temperature distribution
angular distortion
multi-objective functions
title_short Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints
title_full Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints
title_fullStr Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints
title_full_unstemmed Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints
title_sort Using Genetic Algorithms With Multi-Objective Optimization To Adjust Finite Element Models Of Welded Joints
dc.creator.none.fl_str_mv Lostado Lorza, Rubén
Escribano García, Rubén
Fernández Martínez, Roberto
Martínez Calvo, María Ángeles
author Lostado Lorza, Rubén
author_facet Lostado Lorza, Rubén
Escribano García, Rubén
Fernández Martínez, Roberto
Martínez Calvo, María Ángeles
author_role author
author2 Escribano García, Rubén
Fernández Martínez, Roberto
Martínez Calvo, María Ángeles
author2_role author
author
author
dc.subject.none.fl_str_mv finite element method
genetic algorithms
welding temperature distribution
angular distortion
multi-objective functions
topic finite element method
genetic algorithms
welding temperature distribution
angular distortion
multi-objective functions
description To ensure realistic results when modeling welded joints using the finite element method (FEM), it is essential to appropriately characterize the thermo-mechanical behavior of the elastic-plastic Finite Element (FE) models. This task is complex. Any small differences between the actual welded joints and the welded joints based on FEM can be amplified enormously in the presence of nonlinearities. Due to the intense concentration of heat on a small area to create such joints, the regions near the weld line undergo severe thermal cycles. These generate significant angular distortion due mainly to the residual stresses. This paper proposes a method to determine the parameters that are most appropriate for modeling the Butt joint single V-groove welded joint FE models' thermo-mechanical behavior that were created by the one-pass Gas Metal Arc Welding (GMAW). The method is based on experimental data, as well as genetic algorithms (GA) with multi-objective functions. As a practical example, the proposed methodology is validated with three different welded joints specimens that are manufactured by different voltages and currents (26 volts and 140 amps, 28 volts and 210 amps, and 35 volts and 260 amps). The electrode orientation, shielding gas flow rate, distance between nozzle and plate, and welding speed were considered to be constant for all of the specimens that were studied, and their values were 80deg, 20.0 L/min, 4.0 mm, and 6 mm/s, respectively. The base material was EN 235JR low carbon steel, whereas the weld bead was ER70S-6 for the three specimens that were welded. An agreement between the temperature field and the angular distortion that was obtained by the adjusted FE models and those that were obtained experimentally demonstrates that the proposed methodology may be valid for automatically determining the most appropriate parameters.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/27777
url http://hdl.handle.net/10810/27777
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://www.mdpi.com/2075-4701/8/4/230
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
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