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dc.contributor.authorPeralta Abadía, José Joaquín
dc.contributor.authorLarrinaga, Felix
dc.contributor.authorCUESTA ZABALAJAUREGUI, MIKEL
dc.contributor.authorBadiola, Xabier
dc.contributor.authorDuo, Aitor
dc.contributor.authorOlalde Mendia, Gorka
dc.date.accessioned2024-11-21T15:57:36Z
dc.date.available2024-11-21T15:57:36Z
dc.date.issued2024
dc.identifier.isbn979-8-3503-6123-0en
dc.identifier.issn1946-0759en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178477en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6813
dc.description.abstractThe manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance.es
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2024 IEEEen
dc.subjectManufacturing industryen
dc.subjectMachiningen
dc.subjectMachine learningen
dc.subjectODS 8 Trabajo decente y crecimiento económicoes
dc.subjectODS 9 Industria, innovación e infraestructuraes
dc.titleOptiTwin: Data-Driven Machining Process Optimization Platform for SMEsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceInternational Conference on Emerging Technologies and Factory Automation (ETFA)en
local.contributor.groupIngeniería del software y sistemases
local.contributor.groupMecanizado de alto rendimientoes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1109/ETFA61755.2024.10711032en
local.embargo.enddate2026-10-31
local.source.details29. Padova, 10-13 septiembre, 2024
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
dc.unesco.tesaurohttp://vocabularies.unesco.org/thesaurus/concept5767en
oaire.funderNameComisión Europeaen
oaire.funderNameGobierno Vascoen
oaire.funderNameGobierno Vascoen
oaire.funderIdentifierhttps://ror.org/00k4n6c32 / http://data.crossref.org/fundingdata/funder/10.13039/501100000780en
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086en
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.fundingStreamHorizon 2020en
oaire.fundingStreamIkertalde 2022en
oaire.fundingStreamIkertalde 2022en
oaire.awardNumber814078en
oaire.awardNumberIT1519-22en
oaire.awardNumberIT1443-22en
oaire.awardTitleDigital Manufacturing and Design Training Network (DiManD)en
oaire.awardTitleIngeniería de Software y Sistemasen
oaire.awardTitleMecanizado de Alto Rendimientoen
oaire.awardURIhttps://doi.org/10.3030/814078en
oaire.awardURISin informaciónen
oaire.awardURISin informaciónen
dc.unesco.clasificacionhttp://skos.um.es/unesco6/120305en


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