Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding
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: | http://hdl.handle.net/10362/155103 |
Resumo: | Funding Information: This work was partially supported by the SIMShore: SIMOcean Nearshore Bathymetry Based on Low Cost Approaches. This project received funding from the EEA Grants Portugal research and innovation program under grant agreement No PT-INNOVATION-0027. Publisher Copyright: © 2023 by the authors. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Weldingdata predictionenergy consumptionIndustry 4.0machine learningmanufacturingoptimizationBioengineeringChemical Engineering (miscellaneous)Process Chemistry and TechnologySDG 7 - Affordable and Clean EnergyFunding Information: This work was partially supported by the SIMShore: SIMOcean Nearshore Bathymetry Based on Low Cost Approaches. This project received funding from the EEA Grants Portugal research and innovation program under grant agreement No PT-INNOVATION-0027. Publisher Copyright: © 2023 by the authors.The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce the ecological footprint and production costs. Thus, in this work, a system is developed capable of extracting and evaluating useful data regarding production metrics and outputs. With the extracted data, machine learning-based models were created to predict the expected energy consumption of an automotive spot welding, proving a clear insight into how the input values can contribute to the energy consumption of each product or machine, but also correlate the real values to the ideal ones and use this information to determine if some process is not working as intended. The method is demonstrated in real-world scenarios with robotic cells that meet Volkswagen and Ford standards. The results are promising, as models can accurately predict the expected consumption from the cells and allow managers to infer problems or optimize schedule decisions based on the energy consumption. Additionally, by the nature of the conceived architecture, there is room to expand and build additional systems upon the currently existing software.CTS - Centro de Tecnologia e SistemasDEE - Departamento de Engenharia Electrotécnica e de ComputadoresUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasDCT - Departamento de Ciências da TerraRUNFreitas, NelsonAraújo, Sara OleiroAlemão, DuarteRamos, JoãoGuedes, MagnoGonçalves, JoséPeres, Ricardo SilvaRocha, André DionísioBarata, José2023-07-11T22:22:10Z2023-01-162023-01-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article25application/pdfhttp://hdl.handle.net/10362/155103eng2227-9717PURE: 65874803https://doi.org/10.3390/pr11010284info: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:RCAAP2024-03-11T05:37:34Zoai:run.unl.pt:10362/155103Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:54.542658Repositório 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 |
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding |
title |
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding |
spellingShingle |
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding Freitas, Nelson data prediction energy consumption Industry 4.0 machine learning manufacturing optimization Bioengineering Chemical Engineering (miscellaneous) Process Chemistry and Technology SDG 7 - Affordable and Clean Energy |
title_short |
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding |
title_full |
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding |
title_fullStr |
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding |
title_full_unstemmed |
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding |
title_sort |
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding |
author |
Freitas, Nelson |
author_facet |
Freitas, Nelson Araújo, Sara Oleiro Alemão, Duarte Ramos, João Guedes, Magno Gonçalves, José Peres, Ricardo Silva Rocha, André Dionísio Barata, José |
author_role |
author |
author2 |
Araújo, Sara Oleiro Alemão, Duarte Ramos, João Guedes, Magno Gonçalves, José Peres, Ricardo Silva Rocha, André Dionísio Barata, José |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
CTS - Centro de Tecnologia e Sistemas DEE - Departamento de Engenharia Electrotécnica e de Computadores UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias DCT - Departamento de Ciências da Terra RUN |
dc.contributor.author.fl_str_mv |
Freitas, Nelson Araújo, Sara Oleiro Alemão, Duarte Ramos, João Guedes, Magno Gonçalves, José Peres, Ricardo Silva Rocha, André Dionísio Barata, José |
dc.subject.por.fl_str_mv |
data prediction energy consumption Industry 4.0 machine learning manufacturing optimization Bioengineering Chemical Engineering (miscellaneous) Process Chemistry and Technology SDG 7 - Affordable and Clean Energy |
topic |
data prediction energy consumption Industry 4.0 machine learning manufacturing optimization Bioengineering Chemical Engineering (miscellaneous) Process Chemistry and Technology SDG 7 - Affordable and Clean Energy |
description |
Funding Information: This work was partially supported by the SIMShore: SIMOcean Nearshore Bathymetry Based on Low Cost Approaches. This project received funding from the EEA Grants Portugal research and innovation program under grant agreement No PT-INNOVATION-0027. Publisher Copyright: © 2023 by the authors. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-11T22:22:10Z 2023-01-16 2023-01-16T00:00:00Z |
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 |
http://hdl.handle.net/10362/155103 |
url |
http://hdl.handle.net/10362/155103 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2227-9717 PURE: 65874803 https://doi.org/10.3390/pr11010284 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
25 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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1799138145250312192 |