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dc.contributor.authorGarrido Labrador, José Luis 
dc.contributor.authorPuente Gabarri, Daniel
dc.contributor.authorRamírez Sanz, José Miguel 
dc.contributor.authorAyala Dulanto, David
dc.contributor.authorMaudes Raedo, Jesús M. 
dc.date.accessioned2020-07-16T09:02:31Z
dc.date.available2020-07-16T09:02:31Z
dc.date.issued2020-07
dc.identifier.urihttp://hdl.handle.net/10259/5379
dc.description.abstractThe development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.en
dc.description.sponsorshipProjects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds.es
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofApplied Sciences. 2020, V. 10, n. 13, 4606es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectensembleses
dc.subjectunbalanced datasetsen
dc.subjectinternet of thingsen
dc.subjectrotation forestsen
dc.subjectmillingen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleUsing ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solutionen
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttps://doi.org/10.3390/app10134606
dc.identifier.doi10.3390/app10134606
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/TIN2015-67534-P
dc.relation.projectIDinfo:eu-repo/grantAgreement/JCyL/CCTT1/17/BU/0003
dc.relation.projectIDinfo:eu-repo/grantAgreement/JCyL/BU085P17
dc.identifier.essn2076-3417
dc.journal.titleApplied Scienceses
dc.volume.number10es
dc.issue.number13es
dc.page.initial4606es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion


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