Multistage Quality Control Using Machine Learning in the Automotive Industry

Detalhes bibliográficos
Autor(a) principal: Peres, Ricardo Silva
Data de Publicação: 2019
Outros Autores: Barata, José, Leitão, Paulo, Garcia, Gisela
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/147028
Resumo: FCT/MCTES (UNINOVA-CTS funding UID/EEA/00066/2019)
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oai_identifier_str oai:run.unl.pt:10362/147028
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network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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spelling Multistage Quality Control Using Machine Learning in the Automotive Industryautomotive industryindustry 40Machine learningmultistagepredictive manufacturing systemquality controlComputer Science(all)Materials Science(all)Engineering(all)FCT/MCTES (UNINOVA-CTS funding UID/EEA/00066/2019)Product dimensional variability is a crucial factor in the quality control of complex multistage manufacturing processes, where undetected defects can easily be propagated downstream. The recent advances in information technologies and consequently the increased volume of data that has become readily available provide an excellent opportunity for the development of automated defect detection approaches that are capable of extracting the implicit complex relationships in these multivariate data-rich environments. In this paper, several machine learning classifiers were trained and evaluated on varied metrics to predict dimensional defects in a real automotive multistage assembly line. The line encompasses two automated inspection stages with several human-operated assembly and pre-alignment stages in between. The results show that non-linear models like XGBoost and Random Forests are capable of modelling the complexity of such an environment, achieving a high true positive rate and showing promise for the improvement of existing quality control approaches, enabling defects and deviations to be addressed earlier and thus assist in reducing scrap and repair costs.UNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasRUNPeres, Ricardo SilvaBarata, JoséLeitão, PauloGarcia, Gisela2023-01-05T22:12:17Z2019-01-012019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article9application/pdfhttp://hdl.handle.net/10362/147028engPURE: 15490922https://doi.org/10.1109/ACCESS.2019.2923405info: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:RCAAP2024-05-22T18:07:44Zoai:run.unl.pt:10362/147028Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T18:07:44Repositório 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 Multistage Quality Control Using Machine Learning in the Automotive Industry
title Multistage Quality Control Using Machine Learning in the Automotive Industry
spellingShingle Multistage Quality Control Using Machine Learning in the Automotive Industry
Peres, Ricardo Silva
automotive industry
industry 40
Machine learning
multistage
predictive manufacturing system
quality control
Computer Science(all)
Materials Science(all)
Engineering(all)
title_short Multistage Quality Control Using Machine Learning in the Automotive Industry
title_full Multistage Quality Control Using Machine Learning in the Automotive Industry
title_fullStr Multistage Quality Control Using Machine Learning in the Automotive Industry
title_full_unstemmed Multistage Quality Control Using Machine Learning in the Automotive Industry
title_sort Multistage Quality Control Using Machine Learning in the Automotive Industry
author Peres, Ricardo Silva
author_facet Peres, Ricardo Silva
Barata, José
Leitão, Paulo
Garcia, Gisela
author_role author
author2 Barata, José
Leitão, Paulo
Garcia, Gisela
author2_role author
author
author
dc.contributor.none.fl_str_mv UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
CTS - Centro de Tecnologia e Sistemas
RUN
dc.contributor.author.fl_str_mv Peres, Ricardo Silva
Barata, José
Leitão, Paulo
Garcia, Gisela
dc.subject.por.fl_str_mv automotive industry
industry 40
Machine learning
multistage
predictive manufacturing system
quality control
Computer Science(all)
Materials Science(all)
Engineering(all)
topic automotive industry
industry 40
Machine learning
multistage
predictive manufacturing system
quality control
Computer Science(all)
Materials Science(all)
Engineering(all)
description FCT/MCTES (UNINOVA-CTS funding UID/EEA/00066/2019)
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
2019-01-01T00:00:00Z
2023-01-05T22:12:17Z
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/10362/147028
url http://hdl.handle.net/10362/147028
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 15490922
https://doi.org/10.1109/ACCESS.2019.2923405
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9
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 mluisa.alvim@gmail.com
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