Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/85580 |
Resumo: | Polymer single-screw extrusion is a major industrial processing technique used to obtain plastic products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficient extruder screws and decide whether a conventional screw will perform the job efficiently, or a barrier screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. The processing of two polymers, low-density polyethylene and polypropylene, is analyzed. A methodology based on the use of artificial intelligence (AI) techniques, namely, data mining, decision making, and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP, the use of both types of screws would be feasible. |
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Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision makingPolymer extrusionBarrier screwsMulti-objective optimizationData miningDecision makingNumber of objectives reductionPolymer single-screw extrusion is a major industrial processing technique used to obtain plastic products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficient extruder screws and decide whether a conventional screw will perform the job efficiently, or a barrier screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. The processing of two polymers, low-density polyethylene and polypropylene, is analyzed. A methodology based on the use of artificial intelligence (AI) techniques, namely, data mining, decision making, and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP, the use of both types of screws would be feasible.This research was funded by POR Norte under the PhD Grant PRT/BD/152192/2021. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Program and National Funds through FCT (Portuguese Foundation for Science and Technology) under the pro-jects UID-B/05256/2020 and UID-P/05256/2020, the Center for Mathematical Sciences Applied to Industry (CeMEAI), and the support from the São Paulo Research Foundation (FAPESP grant No 2013/07375-0, the Center for Artificial Intelligence (C4AI-USP), the support from the São Paulo Research Foundation (FAPESP grant No 2019/07665-4), and the IBM Corporation.Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoGaspar-Cunha, A.Costa, PauloDelbem, AlexandreMonaco, FranciscoFerreira, Maria JoséCovas, J. A.2023-05-072023-05-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85580engGaspar-Cunha, A.; Costa, P.; Delbem, A.; Monaco, F.; Ferreira, M.J.; Covas, J. Evolutionary Multi-Objective Optimization of Extrusion Barrier Screws: Data Mining and Decision Making. Polymers 2023, 15, 2212. https://doi.org/10.3390/polym150922122073-436010.3390/polym150922122212https://www.mdpi.com/2073-4360/15/9/2212info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:39:42Zoai:repositorium.sdum.uminho.pt:1822/85580Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:36:21.978971Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making |
title |
Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making |
spellingShingle |
Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making Gaspar-Cunha, A. Polymer extrusion Barrier screws Multi-objective optimization Data mining Decision making Number of objectives reduction |
title_short |
Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making |
title_full |
Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making |
title_fullStr |
Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making |
title_full_unstemmed |
Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making |
title_sort |
Evolutionary multi-objective optimization of extrusion barrier screws: data mining and decision making |
author |
Gaspar-Cunha, A. |
author_facet |
Gaspar-Cunha, A. Costa, Paulo Delbem, Alexandre Monaco, Francisco Ferreira, Maria José Covas, J. A. |
author_role |
author |
author2 |
Costa, Paulo Delbem, Alexandre Monaco, Francisco Ferreira, Maria José Covas, J. A. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Gaspar-Cunha, A. Costa, Paulo Delbem, Alexandre Monaco, Francisco Ferreira, Maria José Covas, J. A. |
dc.subject.por.fl_str_mv |
Polymer extrusion Barrier screws Multi-objective optimization Data mining Decision making Number of objectives reduction |
topic |
Polymer extrusion Barrier screws Multi-objective optimization Data mining Decision making Number of objectives reduction |
description |
Polymer single-screw extrusion is a major industrial processing technique used to obtain plastic products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficient extruder screws and decide whether a conventional screw will perform the job efficiently, or a barrier screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. The processing of two polymers, low-density polyethylene and polypropylene, is analyzed. A methodology based on the use of artificial intelligence (AI) techniques, namely, data mining, decision making, and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP, the use of both types of screws would be feasible. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-07 2023-05-07T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/85580 |
url |
https://hdl.handle.net/1822/85580 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Gaspar-Cunha, A.; Costa, P.; Delbem, A.; Monaco, F.; Ferreira, M.J.; Covas, J. Evolutionary Multi-Objective Optimization of Extrusion Barrier Screws: Data Mining and Decision Making. Polymers 2023, 15, 2212. https://doi.org/10.3390/polym15092212 2073-4360 10.3390/polym15092212 2212 https://www.mdpi.com/2073-4360/15/9/2212 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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