Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding

Detalhes bibliográficos
Autor(a) principal: Freitas, Nelson
Data de Publicação: 2023
Outros Autores: Araújo, Sara Oleiro, Alemão, Duarte, Ramos, João, Guedes, Magno, Gonçalves, José, Peres, Ricardo Silva, Rocha, André Dionísio, Barata, José
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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spelling 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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