Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization

This manuscript presents an efficient multi-objective optimization method based on using particle swarm optimization together with a desirability function that can be applied where the response variables may have an opposite behavior and where the range of variation of the independent variables as w...

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Detalles Bibliográficos
Autor: Luis Pérez, Carmelo Javier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/47669
Acceso en línea:https://hdl.handle.net/2454/47669
Access Level:acceso abierto
Palabra clave:Multi-objective optimization
Manufacturing
Fuzzy modeling
PSO
ANFIS
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spelling Multi-objective optimization of electrical discharge machining parameters using particle swarm optimizationLuis Pérez, Carmelo JavierMulti-objective optimizationManufacturingFuzzy modelingPSOANFISThis manuscript presents an efficient multi-objective optimization method based on using particle swarm optimization together with a desirability function that can be applied where the response variables may have an opposite behavior and where the range of variation of the independent variables as well as those of the responses are subjected to constraints, which has a great deal of industrial interest. For example, maintaining roughness and dimensional tolerances within a tolerance range is determined by the design requirements of the manufactured parts (shape errors, microgeometry errors, etc.) and these requirements must be met in the manufacture of parts. It is demonstrated that it is possible to obtain optimal results in the ranges of variation considered for the independent variables, with regard to those obtained by experimentation. Similarly, models based on Adaptive Network-based Fuzzy Inference Systems are used to solve the problem that may arise from the inadequate fitting of the regression models. Thus, thanks to this present study a fast and efficient method is available for the multiple-optimization of response variables, subject to constraints on both response and independent variables, which are obtained from experiments and modelled by means of soft computing techniques. Furthermore, it is also demonstrated that it is possible to obtain technology tables for various manufacturing processes, which is of great interest from a technological point of view so as to obtain the most suitable processing conditions.Open access funding provided by Universidad Pública de Navarra.ElsevierIngenieríaIngeniaritzaUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/47669reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglés© 2024 The Author(s). This is an open access article under the CC BY-NC-ND license.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/476692026-06-17T12:41:47Z
dc.title.none.fl_str_mv Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
title Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
spellingShingle Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
Luis Pérez, Carmelo Javier
Multi-objective optimization
Manufacturing
Fuzzy modeling
PSO
ANFIS
title_short Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
title_full Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
title_fullStr Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
title_full_unstemmed Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
title_sort Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
dc.creator.none.fl_str_mv Luis Pérez, Carmelo Javier
author Luis Pérez, Carmelo Javier
author_facet Luis Pérez, Carmelo Javier
author_role author
dc.contributor.none.fl_str_mv Ingeniería
Ingeniaritza
Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
dc.subject.none.fl_str_mv Multi-objective optimization
Manufacturing
Fuzzy modeling
PSO
ANFIS
topic Multi-objective optimization
Manufacturing
Fuzzy modeling
PSO
ANFIS
description This manuscript presents an efficient multi-objective optimization method based on using particle swarm optimization together with a desirability function that can be applied where the response variables may have an opposite behavior and where the range of variation of the independent variables as well as those of the responses are subjected to constraints, which has a great deal of industrial interest. For example, maintaining roughness and dimensional tolerances within a tolerance range is determined by the design requirements of the manufactured parts (shape errors, microgeometry errors, etc.) and these requirements must be met in the manufacture of parts. It is demonstrated that it is possible to obtain optimal results in the ranges of variation considered for the independent variables, with regard to those obtained by experimentation. Similarly, models based on Adaptive Network-based Fuzzy Inference Systems are used to solve the problem that may arise from the inadequate fitting of the regression models. Thus, thanks to this present study a fast and efficient method is available for the multiple-optimization of response variables, subject to constraints on both response and independent variables, which are obtained from experiments and modelled by means of soft computing techniques. Furthermore, it is also demonstrated that it is possible to obtain technology tables for various manufacturing processes, which is of great interest from a technological point of view so as to obtain the most suitable processing conditions.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/47669
url https://hdl.handle.net/2454/47669
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv © 2024 The Author(s). This is an open access article under the CC BY-NC-ND license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2024 The Author(s). This is an open access article under the CC BY-NC-ND license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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