Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts

[EN] Injection molding is a fundamental process for transforming plastics into various industrial components. Among the critical aspects studied in this process, volumetric contraction and warpage of plastic parts are of particular importance. Achieving precise control over warpage is crucial for en...

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Autores: Gámez Martínez, Juan Luís, Jorda-Vilaplana, Amparo, Peydro, M. A.|||0000-0002-8503-1505, Sellés, M.A.|||0000-0002-0784-5757, Sanchez-Caballero, Samuel|||0000-0001-5322-8082
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:dnet:riunet______::b30bb792ede6371a2dfd5c790da6d70b
Acceso en línea:https://riunet.upv.es/handle/10251/234657
Access Level:acceso abierto
Palabra clave:Molding
Volumetric contraction
Warpage
Artificial neural network
Optimization
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spelling Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular PartsGámez Martínez, Juan LuísJorda-Vilaplana, AmparoPeydro, M. A.|||0000-0002-8503-1505Sellés, M.A.|||0000-0002-0784-5757Sanchez-Caballero, Samuel|||0000-0001-5322-8082MoldingVolumetric contractionWarpageArtificial neural networkOptimization[EN] Injection molding is a fundamental process for transforming plastics into various industrial components. Among the critical aspects studied in this process, volumetric contraction and warpage of plastic parts are of particular importance. Achieving precise control over warpage is crucial for ensuring the production of high-quality components. This research explores optimizing injection process parameters to minimize volumetric contraction and warpage in rectangular polypropylene (PP) parts. The study employs experimental analysis, MoldFlow simulation, and Artificial Neural Network (ANN) modeling. MoldFlow simulation software provides valuable data on warpage, serving as input for the ANN model. Based on the Backpropagation Neural Network algorithm, the optimized ANN model accurately predicts warpage by considering factors such as part thickness, flow path distance, and flow path tangent. The study highlights the importance of accurately setting injection parameters to achieve optimal warpage results. The BPNN-based approach offers a faster and more efficient alternative to computer-aided engineering (CAE) processes for studying warpage.The authors thank the Vicerrectorado de Investigacion de la Universitat Politecnica de Valencia (PAID-11-24).MDPIDepartamento de Ingeniería GráficaDepartamento de Ingeniería Mecánica y de MaterialesInstituto de Diseño para la Fabricación y Producción Automatizada Instituto Universitario de Investigación de Tecnología de los Materiales de la UPVEscuela Politécnica Superior de AlcoyUNIVERSIDAD POLITECNICA DE VALENCIARepositorio Institucional de la Universitat Politècnica de València Riunet20252025-07-09journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/234657reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengUPV-VIN UPV-VIN PAID-11-24 Impresión aditiva in situ de palas de aerogenerador (EOLO)open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:riunet______::b30bb792ede6371a2dfd5c790da6d70b2026-06-13T07:49:27Z
dc.title.none.fl_str_mv Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
title Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
spellingShingle Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
Gámez Martínez, Juan Luís
Molding
Volumetric contraction
Warpage
Artificial neural network
Optimization
title_short Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
title_full Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
title_fullStr Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
title_full_unstemmed Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
title_sort Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
dc.creator.none.fl_str_mv Gámez Martínez, Juan Luís
Jorda-Vilaplana, Amparo
Peydro, M. A.|||0000-0002-8503-1505
Sellés, M.A.|||0000-0002-0784-5757
Sanchez-Caballero, Samuel|||0000-0001-5322-8082
author Gámez Martínez, Juan Luís
author_facet Gámez Martínez, Juan Luís
Jorda-Vilaplana, Amparo
Peydro, M. A.|||0000-0002-8503-1505
Sellés, M.A.|||0000-0002-0784-5757
Sanchez-Caballero, Samuel|||0000-0001-5322-8082
author_role author
author2 Jorda-Vilaplana, Amparo
Peydro, M. A.|||0000-0002-8503-1505
Sellés, M.A.|||0000-0002-0784-5757
Sanchez-Caballero, Samuel|||0000-0001-5322-8082
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Gráfica
Departamento de Ingeniería Mecánica y de Materiales
Instituto de Diseño para la Fabricación y Producción Automatizada
 Instituto Universitario de Investigación de Tecnología de los Materiales de la UPV
Escuela Politécnica Superior de Alcoy
UNIVERSIDAD POLITECNICA DE VALENCIA
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Molding
Volumetric contraction
Warpage
Artificial neural network
Optimization
topic Molding
Volumetric contraction
Warpage
Artificial neural network
Optimization
description [EN] Injection molding is a fundamental process for transforming plastics into various industrial components. Among the critical aspects studied in this process, volumetric contraction and warpage of plastic parts are of particular importance. Achieving precise control over warpage is crucial for ensuring the production of high-quality components. This research explores optimizing injection process parameters to minimize volumetric contraction and warpage in rectangular polypropylene (PP) parts. The study employs experimental analysis, MoldFlow simulation, and Artificial Neural Network (ANN) modeling. MoldFlow simulation software provides valuable data on warpage, serving as input for the ANN model. Based on the Backpropagation Neural Network algorithm, the optimized ANN model accurately predicts warpage by considering factors such as part thickness, flow path distance, and flow path tangent. The study highlights the importance of accurately setting injection parameters to achieve optimal warpage results. The BPNN-based approach offers a faster and more efficient alternative to computer-aided engineering (CAE) processes for studying warpage.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-07-09
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://riunet.upv.es/handle/10251/234657
url https://riunet.upv.es/handle/10251/234657
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv UPV-VIN UPV-VIN PAID-11-24 Impresión aditiva in situ de palas de aerogenerador (EOLO)
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/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
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
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:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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