Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Detalhes bibliográficos
Autor(a) principal: Silva, Bruno
Data de Publicação: 2022
Outros Autores: Marques, Ruben, Faustino, Dinis, Ilheu, Paulo, Santos, Tiago, Sousa, João, Rocha, André Dionisio
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/150797
Resumo: Publisher Copyright: © 2022 by the authors.
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spelling Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect ManufacturingArtificial IntelligenceData AugmentationHuman-in-the-Loop labelinginjection moldingOEEpredictive qualityBioengineeringChemical Engineering (miscellaneous)Process Chemistry and TechnologySDG 12 - Responsible Consumption and ProductionPublisher Copyright: © 2022 by the authors.With the spread of the Industry 4.0 concept, implementing Artificial Intelligence approaches on the shop floor that allow companies to increase their competitiveness in the market is starting to be prioritized. Due to the complexity of the processes used in the industry, the inclusion of a real-time Quality Prediction methodology avoids a considerable number of costs to companies. This paper exposes the whole process of introducing Artificial Intelligence in plastic injection molding processes in a company in Portugal. All the implementations and methodologies used are presented, from data collection to real-time classification, such as Data Augmentation and Human-in-the-Loop labeling, among others. This approach also allows predicting and alerting with regard to process quality loss. This leads to a reduction in the production of non-compliant parts, which increases productivity and reduces costs and environmental footprint. In order to understand the applicability of this system, it was tested in different injection molding processes (traditional and stretch and blow) and with different materials and products. The results of this document show that, with the approach developed and presented, it was possible to achieve an increase in Overall Equipment Effectiveness (OEE) of up to 12%, a reduction in the process downtime of up to 9% and a significant reduction in the number of non-conforming parts produced. This improvement in key performance indicators proves the potential of this solution.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasRUNSilva, BrunoMarques, RubenFaustino, DinisIlheu, PauloSantos, TiagoSousa, JoãoRocha, André Dionisio2023-03-17T22:30:57Z2022-12-272022-12-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article24application/pdfhttp://hdl.handle.net/10362/150797eng2227-9717PURE: 56193559https://doi.org/10.3390/pr11010062info: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:RCAAP2024-03-11T05:33:13Zoai:run.unl.pt:10362/150797Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:19.468369Repositório 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 Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
title Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
spellingShingle Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
Silva, Bruno
Artificial Intelligence
Data Augmentation
Human-in-the-Loop labeling
injection molding
OEE
predictive quality
Bioengineering
Chemical Engineering (miscellaneous)
Process Chemistry and Technology
SDG 12 - Responsible Consumption and Production
title_short Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
title_full Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
title_fullStr Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
title_full_unstemmed Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
title_sort Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
author Silva, Bruno
author_facet Silva, Bruno
Marques, Ruben
Faustino, Dinis
Ilheu, Paulo
Santos, Tiago
Sousa, João
Rocha, André Dionisio
author_role author
author2 Marques, Ruben
Faustino, Dinis
Ilheu, Paulo
Santos, Tiago
Sousa, João
Rocha, André Dionisio
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv DEE - Departamento de Engenharia Electrotécnica e de Computadores
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
CTS - Centro de Tecnologia e Sistemas
RUN
dc.contributor.author.fl_str_mv Silva, Bruno
Marques, Ruben
Faustino, Dinis
Ilheu, Paulo
Santos, Tiago
Sousa, João
Rocha, André Dionisio
dc.subject.por.fl_str_mv Artificial Intelligence
Data Augmentation
Human-in-the-Loop labeling
injection molding
OEE
predictive quality
Bioengineering
Chemical Engineering (miscellaneous)
Process Chemistry and Technology
SDG 12 - Responsible Consumption and Production
topic Artificial Intelligence
Data Augmentation
Human-in-the-Loop labeling
injection molding
OEE
predictive quality
Bioengineering
Chemical Engineering (miscellaneous)
Process Chemistry and Technology
SDG 12 - Responsible Consumption and Production
description Publisher Copyright: © 2022 by the authors.
publishDate 2022
dc.date.none.fl_str_mv 2022-12-27
2022-12-27T00:00:00Z
2023-03-17T22:30:57Z
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 http://hdl.handle.net/10362/150797
url http://hdl.handle.net/10362/150797
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2227-9717
PURE: 56193559
https://doi.org/10.3390/pr11010062
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 24
application/pdf
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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution 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
repository.mail.fl_str_mv
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