Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling

This paper aims to investigate the effect of FFF 3D printing parameters on surface roughness, dimensional error and density of glass fibre-filled polypropylene (GF/PP) parts, which is a promising material to be used to obtain surgical models for bones, due to its high thermal and mechanical properti...

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Autores: Luis Pérez, Carmelo Javier, Buj Corral, Irene|||0000-0003-4058-4162
Tipo de recurso: artículo
Fecha de publicación:2025
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:2117/441266
Acceso en línea:https://hdl.handle.net/2117/441266
https://dx.doi.org/10.1108/RPJ-11-2024-0485
Access Level:acceso abierto
Palabra clave:Fuzzy modeling
Regression
ANFIS
Optimization
Roughness
Dimensional accuracy
Density
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomaterials
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spelling Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modelingLuis Pérez, Carmelo JavierBuj Corral, Irene|||0000-0003-4058-4162Fuzzy modelingRegressionANFISOptimizationRoughnessDimensional accuracyDensityÀrees temàtiques de la UPC::Enginyeria biomèdica::BiomaterialsThis paper aims to investigate the effect of FFF 3D printing parameters on surface roughness, dimensional error and density of glass fibre-filled polypropylene (GF/PP) parts, which is a promising material to be used to obtain surgical models for bones, due to its high thermal and mechanical properties. The experimental approach focuses on the manufacture of cuboid parts, measurement of surface roughness, dimensions and weight, and the use of ANFIS models to analyse the influence of 3D printing parameters such as printing temperature, print speed, nozzle diameter and layer height on the responses. Multi-objective optimization by means of the desirability function is then applied to obtain optimal parameters for the 3D printing parameters. The research shows the optimal values for the 3D printing parameters that are recommended in order to simultaneously minimize roughness, dimensional error and to maximize density. Specifically, a low temperature of 230 ºC, a medium print speed of 20 mm/s, a high nozzle diameter of 0.8 mm and a low layer height of 0.1 mm were found to be optimal. This present study will help select appropriate 3D printing parameters when printing surgical models in glass fibre-filled PP materials, which have recently become commercially available in FFF processes and, thus, have been scarcely studied in the literature for their use in surgical models.This research was financed by the Spanish Agencia Estatal de Investigación (AEI) with grant PID2020-115647RB-C21, as well as by the Centre de Cooperació i Desenvolupament of Universitat Politècnica de Catalunya (CCD-UPC) with grant CCD-2023-B011.Peer Reviewed20252025-08-0720252025-09-08journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/441266https://dx.doi.org/10.1108/RPJ-11-2024-0485reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-115647RB-C21 OBTENCION DE PROTESIS PARA SUSTITUCION DE TEJIDO OSEO MEDIANTE IMPRESION 3D POR EXTRUSION Y POSTERIOR SINTERIZADO.open accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4412662026-05-27T15:37:01Z
dc.title.none.fl_str_mv Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling
title Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling
spellingShingle Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling
Luis Pérez, Carmelo Javier
Fuzzy modeling
Regression
ANFIS
Optimization
Roughness
Dimensional accuracy
Density
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomaterials
title_short Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling
title_full Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling
title_fullStr Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling
title_full_unstemmed Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling
title_sort Multi-objective optimization of surface roughness, dimensional errors and density in FFF 3D-printed glass fiber-reinforced PP parts via adaptive neuro-fuzzy inference modeling
dc.creator.none.fl_str_mv Luis Pérez, Carmelo Javier
Buj Corral, Irene|||0000-0003-4058-4162
author Luis Pérez, Carmelo Javier
author_facet Luis Pérez, Carmelo Javier
Buj Corral, Irene|||0000-0003-4058-4162
author_role author
author2 Buj Corral, Irene|||0000-0003-4058-4162
author2_role author
dc.subject.none.fl_str_mv Fuzzy modeling
Regression
ANFIS
Optimization
Roughness
Dimensional accuracy
Density
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomaterials
topic Fuzzy modeling
Regression
ANFIS
Optimization
Roughness
Dimensional accuracy
Density
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomaterials
description This paper aims to investigate the effect of FFF 3D printing parameters on surface roughness, dimensional error and density of glass fibre-filled polypropylene (GF/PP) parts, which is a promising material to be used to obtain surgical models for bones, due to its high thermal and mechanical properties. The experimental approach focuses on the manufacture of cuboid parts, measurement of surface roughness, dimensions and weight, and the use of ANFIS models to analyse the influence of 3D printing parameters such as printing temperature, print speed, nozzle diameter and layer height on the responses. Multi-objective optimization by means of the desirability function is then applied to obtain optimal parameters for the 3D printing parameters. The research shows the optimal values for the 3D printing parameters that are recommended in order to simultaneously minimize roughness, dimensional error and to maximize density. Specifically, a low temperature of 230 ºC, a medium print speed of 20 mm/s, a high nozzle diameter of 0.8 mm and a low layer height of 0.1 mm were found to be optimal. This present study will help select appropriate 3D printing parameters when printing surgical models in glass fibre-filled PP materials, which have recently become commercially available in FFF processes and, thus, have been scarcely studied in the literature for their use in surgical models.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-08-07
2025
2025-09-08
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/441266
https://dx.doi.org/10.1108/RPJ-11-2024-0485
url https://hdl.handle.net/2117/441266
https://dx.doi.org/10.1108/RPJ-11-2024-0485
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-115647RB-C21 OBTENCION DE PROTESIS PARA SUSTITUCION DE TEJIDO OSEO MEDIANTE IMPRESION 3D POR EXTRUSION Y POSTERIOR SINTERIZADO.
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
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-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
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 15.811543

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