A Recurrent Neural Network for Warpage Prediction in Injection Molding

Injection molding is classified as one of the most flexible and economical manufacturing processes with high volumeof plastic molded parts. Causes of variations in the process are related to the vast number of factors acting during aregular production run, which directly impacts the quality of final...

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Detalhes bibliográficos
Autores: Alvarado-Iniesta, A., Valles-Rosales, D.J., García-Alcaraz, J.L., Maldonado-Macias, A.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2012
País:México
Recursos:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
Repositorio:Journal of Applied Research and Technology
Idioma:inglés
OAI Identifier:oai:ojs2.localhost:article/351
Acesso em linha:https://jart.icat.unam.mx/index.php/jart/article/view/351
Access Level:acceso abierto
Palavra-chave:Artificial neural network
recurrent neural network
plastic injection molding
warpage prediction.
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spelling A Recurrent Neural Network for Warpage Prediction in Injection MoldingAlvarado-Iniesta, A.Valles-Rosales, D.J.García-Alcaraz, J.L.Maldonado-Macias, A.Artificial neural networkrecurrent neural networkplastic injection moldingwarpage prediction.Injection molding is classified as one of the most flexible and economical manufacturing processes with high volumeof plastic molded parts. Causes of variations in the process are related to the vast number of factors acting during aregular production run, which directly impacts the quality of final products. A common quality trouble in finishedproducts is the presence of warpage. Thus, this study aimed to design a system based on recurrent neural networksto predict warpage defects in products manufactured through injection molding. Five process parameters areemployed for being considered to be critical and have a great impact on the warpage of plastic components. Thisstudy used the finite element analysis software Moldflow to simulate the injection molding process to collect data inorder to train and test the recurrent neural network. Recurrent neural networks were used to understand the dynamicsof the process and due to their memorization ability, warpage values might be predicted accurately. Results show thedesigned network works well in prediction tasks, overcoming those predictions generated by feedforward neuralnetworks.Universidad Nacional Autónoma de México2012-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://jart.icat.unam.mx/index.php/jart/article/view/35110.22201/icat.16656423.2012.10.6.351Journal of Applied Research and Technology; Vol. 10 No. 6Journal of Applied Research and Technology; Vol. 10 Núm. 62448-67361665-642310.22201/icat.24486736e.2012.10.6reponame:Journal of Applied Research and Technologyinstname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICOinstacron:UNAMenghttps://jart.icat.unam.mx/index.php/jart/article/view/351/348Copyright (c) 2018 Journal of Applied Research and Technologyinfo:eu-repo/semantics/openAccessoai:ojs2.localhost:article/3512024-08-16T17:54:14Z
dc.title.none.fl_str_mv A Recurrent Neural Network for Warpage Prediction in Injection Molding
title A Recurrent Neural Network for Warpage Prediction in Injection Molding
spellingShingle A Recurrent Neural Network for Warpage Prediction in Injection Molding
Alvarado-Iniesta, A.
Artificial neural network
recurrent neural network
plastic injection molding
warpage prediction.
title_short A Recurrent Neural Network for Warpage Prediction in Injection Molding
title_full A Recurrent Neural Network for Warpage Prediction in Injection Molding
title_fullStr A Recurrent Neural Network for Warpage Prediction in Injection Molding
title_full_unstemmed A Recurrent Neural Network for Warpage Prediction in Injection Molding
title_sort A Recurrent Neural Network for Warpage Prediction in Injection Molding
dc.creator.none.fl_str_mv Alvarado-Iniesta, A.
Valles-Rosales, D.J.
García-Alcaraz, J.L.
Maldonado-Macias, A.
author Alvarado-Iniesta, A.
author_facet Alvarado-Iniesta, A.
Valles-Rosales, D.J.
García-Alcaraz, J.L.
Maldonado-Macias, A.
author_role author
author2 Valles-Rosales, D.J.
García-Alcaraz, J.L.
Maldonado-Macias, A.
author2_role author
author
author
dc.subject.none.fl_str_mv Artificial neural network
recurrent neural network
plastic injection molding
warpage prediction.
topic Artificial neural network
recurrent neural network
plastic injection molding
warpage prediction.
description Injection molding is classified as one of the most flexible and economical manufacturing processes with high volumeof plastic molded parts. Causes of variations in the process are related to the vast number of factors acting during aregular production run, which directly impacts the quality of final products. A common quality trouble in finishedproducts is the presence of warpage. Thus, this study aimed to design a system based on recurrent neural networksto predict warpage defects in products manufactured through injection molding. Five process parameters areemployed for being considered to be critical and have a great impact on the warpage of plastic components. Thisstudy used the finite element analysis software Moldflow to simulate the injection molding process to collect data inorder to train and test the recurrent neural network. Recurrent neural networks were used to understand the dynamicsof the process and due to their memorization ability, warpage values might be predicted accurately. Results show thedesigned network works well in prediction tasks, overcoming those predictions generated by feedforward neuralnetworks.
publishDate 2012
dc.date.none.fl_str_mv 2012-12-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://jart.icat.unam.mx/index.php/jart/article/view/351
10.22201/icat.16656423.2012.10.6.351
url https://jart.icat.unam.mx/index.php/jart/article/view/351
identifier_str_mv 10.22201/icat.16656423.2012.10.6.351
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://jart.icat.unam.mx/index.php/jart/article/view/351/348
dc.rights.none.fl_str_mv Copyright (c) 2018 Journal of Applied Research and Technology
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2018 Journal of Applied Research and Technology
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional Autónoma de México
publisher.none.fl_str_mv Universidad Nacional Autónoma de México
dc.source.none.fl_str_mv Journal of Applied Research and Technology; Vol. 10 No. 6
Journal of Applied Research and Technology; Vol. 10 Núm. 6
2448-6736
1665-6423
10.22201/icat.24486736e.2012.10.6
reponame:Journal of Applied Research and Technology
instname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron:UNAM
instname_str UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron_str UNAM
institution UNAM
reponame_str Journal of Applied Research and Technology
collection Journal of Applied Research and Technology
repository.name.fl_str_mv
repository.mail.fl_str_mv
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