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dc.contributor.authorFabio Lima
dc.contributor.authorNONOGAKI, L. K. B. Y.
dc.contributor.authorCHANG, J.
dc.contributor.authorAlexandre Augusto Massote
dc.date.accessioned2022-11-01T06:04:29Z
dc.date.accessioned2026-04-28T15:49:32Z
dc.date.available2022-11-01T06:04:29Z
dc.date.available2026-04-28T15:49:32Z
dc.date.issued2022-01-05
dc.identifier.citationLIMA, F.; NONOGAKI, L. K. B. Y.; CHANG, J.; MASSOTE, A. A. Estimation of Energy Consumption in Manufacturing Lines Using Machine Learning into Industry 4.0 Context. PICMET 2022 - Portland International Conference on Management of Engineering and Technology: Technology Management and Leadership in Digital Transformation - Looking Ahead to Post-COVID Era, Proceedings, 2022.
dc.identifier.urihttps://hdl.handle.net/20.500.12032/186907
dc.description.abstract© 2022 PICMET.This paper deals with the simulation of production lines focusing on opportunities of reducing the energy consumption. The simulation of systems is one of the pillars of the so-called Industry 4.0. It has been used a digital manufacturing software, which allow the creation of digital twins, to carry out the models. Once the model has been created and validated, a machine learning approach, more specifically a Neural Network was trained to estimate the energy consumption of the line. The estimation of the energy consumption allows to use this variable to take decisions of the production scheduling. Moreover, the neural network package is embedded into the digital manufacturing software which provides more flexibility to solve the problem using one single software tool. The results validate the proposal, and, for future work, the effective creation of the digital twin should be performed.
dc.relation.ispartofPICMET 2022 - Portland International Conference on Management of Engineering and Technology: Technology Management and Leadership in Digital Transformation - Looking Ahead to Post-COVID Era, Proceedings
dc.rightsAcesso Restrito
dc.titleEstimation of Energy Consumption in Manufacturing Lines Using Machine Learning into Industry 4.0 Context
dc.typeArtigo de evento
dc.identifier.doi10.23919/PICMET53225.2022.9882886
dc.contributor.authorOrcidhttps://orcid.org/0000-0002-5500-3191
dc.contributor.authorOrcidhttps://orcid.org/0000-0002-3467-1879
fei.scopus.citations0
fei.scopus.eid2-s2.0-85139096411
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85139096411&origin=inward
fei.scopus.updated2026-01-27
fei.scopus.subjectDigital manufacturing
fei.scopus.subjectEnergy-consumption
fei.scopus.subjectLine focusing
fei.scopus.subjectMachine learning approaches
fei.scopus.subjectMachine-learning
fei.scopus.subjectManufacturing lines
fei.scopus.subjectManufacturing software
fei.scopus.subjectNeural-networks
fei.scopus.subjectProduction line
fei.scopus.subjectProduction Scheduling


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