Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes

This study focuses on obtaining regression models for material removal rate and tool wear in rough honing processes. For this purpose, experimental tests were carried out according to a central composite design of experiments. Five different parameters were varied: grain size or particle size of abr...

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Detalles Bibliográficos
Autores: Buj Corral, Irene|||0000-0003-4058-4162, Sivatte Adroer, Mauricio|||0000-0002-3064-9682
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/365454
Acceso en línea:https://hdl.handle.net/2117/365454
https://dx.doi.org/10.3390/machines10020083
Access Level:acceso abierto
Palabra clave:Honing
Material removal rate
Tool wear
Regression models
Multi-objective optimization
Brunyiment
Àrees temàtiques de la UPC::Enginyeria mecànica
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spelling Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing ProcessesBuj Corral, Irene|||0000-0003-4058-4162Sivatte Adroer, Mauricio|||0000-0002-3064-9682HoningHoningMaterial removal rateTool wearRegression modelsMulti-objective optimizationBrunyimentÀrees temàtiques de la UPC::Enginyeria mecànicaThis study focuses on obtaining regression models for material removal rate and tool wear in rough honing processes. For this purpose, experimental tests were carried out according to a central composite design of experiments. Five different parameters were varied: grain size or particle size of abrasive, density of abrasive or abrasive concentration, pressure of the stones against the cylinder internal surface, tangential speed (in this case, corresponding to the rotation speed of the cylinder), and linear speed of the honing head. In addition, multi-objective optimization was carried out with the aim of maximizing the material removal rate and minimizing tool wear. The results show that, within the range studied, the material removal rate depends mainly on tangential speed, followed by grain size and pressure. Tool wear is directly influenced by density of abrasive, followed by pressure, tangential speed, and grain size. According to the multi-objective optimization, if the two responses are given the same importance, it is recommended that high grain size, high density, high tangential speed, and low pressure be selected. Linear speed has less influence on both responses studied. If the material removal rate is considered to be more preponderant than tool wear, then the same values should be considered, except for high pressure. If tool wear is preponderant, then lower grain size of 128 (ISO 6106) should be selected, and lower tangential speed of approximately 166 min-1. The other variables, density and pressure, would not change significantly from the first situation.Peer Reviewed20222022-01-2420222022-04-06journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/365454https://dx.doi.org/10.3390/machines10020083reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3654542026-05-27T15:37:01Z
dc.title.none.fl_str_mv Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes
title Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes
spellingShingle Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes
Buj Corral, Irene|||0000-0003-4058-4162
Honing
Honing
Material removal rate
Tool wear
Regression models
Multi-objective optimization
Brunyiment
Àrees temàtiques de la UPC::Enginyeria mecànica
title_short Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes
title_full Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes
title_fullStr Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes
title_full_unstemmed Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes
title_sort Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes
dc.creator.none.fl_str_mv Buj Corral, Irene|||0000-0003-4058-4162
Sivatte Adroer, Mauricio|||0000-0002-3064-9682
author Buj Corral, Irene|||0000-0003-4058-4162
author_facet Buj Corral, Irene|||0000-0003-4058-4162
Sivatte Adroer, Mauricio|||0000-0002-3064-9682
author_role author
author2 Sivatte Adroer, Mauricio|||0000-0002-3064-9682
author2_role author
dc.subject.none.fl_str_mv Honing
Honing
Material removal rate
Tool wear
Regression models
Multi-objective optimization
Brunyiment
Àrees temàtiques de la UPC::Enginyeria mecànica
topic Honing
Honing
Material removal rate
Tool wear
Regression models
Multi-objective optimization
Brunyiment
Àrees temàtiques de la UPC::Enginyeria mecànica
description This study focuses on obtaining regression models for material removal rate and tool wear in rough honing processes. For this purpose, experimental tests were carried out according to a central composite design of experiments. Five different parameters were varied: grain size or particle size of abrasive, density of abrasive or abrasive concentration, pressure of the stones against the cylinder internal surface, tangential speed (in this case, corresponding to the rotation speed of the cylinder), and linear speed of the honing head. In addition, multi-objective optimization was carried out with the aim of maximizing the material removal rate and minimizing tool wear. The results show that, within the range studied, the material removal rate depends mainly on tangential speed, followed by grain size and pressure. Tool wear is directly influenced by density of abrasive, followed by pressure, tangential speed, and grain size. According to the multi-objective optimization, if the two responses are given the same importance, it is recommended that high grain size, high density, high tangential speed, and low pressure be selected. Linear speed has less influence on both responses studied. If the material removal rate is considered to be more preponderant than tool wear, then the same values should be considered, except for high pressure. If tool wear is preponderant, then lower grain size of 128 (ISO 6106) should be selected, and lower tangential speed of approximately 166 min-1. The other variables, density and pressure, would not change significantly from the first situation.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-24
2022
2022-04-06
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/365454
https://dx.doi.org/10.3390/machines10020083
url https://hdl.handle.net/2117/365454
https://dx.doi.org/10.3390/machines10020083
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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