dc.contributor.author | Peralta Abadía, José Joaquín | |
dc.contributor.author | Larrinaga, Felix | |
dc.contributor.author | CUESTA ZABALAJAUREGUI, MIKEL | |
dc.contributor.author | Badiola, Xabier | |
dc.contributor.author | Duo, Aitor | |
dc.contributor.author | Olalde Mendia, Gorka | |
dc.date.accessioned | 2024-11-21T15:57:36Z | |
dc.date.available | 2024-11-21T15:57:36Z | |
dc.date.issued | 2024 | |
dc.identifier.isbn | 979-8-3503-6123-0 | en |
dc.identifier.issn | 1946-0759 | en |
dc.identifier.other | https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178477 | en |
dc.identifier.uri | https://hdl.handle.net/20.500.11984/6813 | |
dc.description.abstract | The 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.iso | eng | en |
dc.publisher | IEEE | en |
dc.rights | © 2024 IEEE | en |
dc.subject | Manufacturing industry | en |
dc.subject | Machining | en |
dc.subject | Machine learning | en |
dc.subject | ODS 8 Trabajo decente y crecimiento económico | es |
dc.subject | ODS 9 Industria, innovación e infraestructura | es |
dc.title | OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs | en |
dcterms.accessRights | http://purl.org/coar/access_right/c_f1cf | en |
dcterms.source | International Conference on Emerging Technologies and Factory Automation (ETFA) | en |
local.contributor.group | Ingeniería del software y sistemas | es |
local.contributor.group | Mecanizado de alto rendimiento | es |
local.description.peerreviewed | true | en |
local.identifier.doi | https://doi.org/10.1109/ETFA61755.2024.10711032 | en |
local.embargo.enddate | 2026-10-31 | |
local.source.details | 29. Padova, 10-13 septiembre, 2024 | |
oaire.format.mimetype | application/pdf | en |
oaire.file | $DSPACE\assetstore | en |
oaire.resourceType | http://purl.org/coar/resource_type/c_c94f | en |
oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | en |
dc.unesco.tesauro | http://vocabularies.unesco.org/thesaurus/concept5767 | en |
oaire.funderName | Comisión Europea | en |
oaire.funderName | Gobierno Vasco | en |
oaire.funderName | Gobierno Vasco | en |
oaire.funderIdentifier | https://ror.org/00k4n6c32 / http://data.crossref.org/fundingdata/funder/10.13039/501100000780 | en |
oaire.funderIdentifier | https://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086 | en |
oaire.funderIdentifier | https://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086 | |
oaire.fundingStream | Horizon 2020 | en |
oaire.fundingStream | Ikertalde 2022 | en |
oaire.fundingStream | Ikertalde 2022 | en |
oaire.awardNumber | 814078 | en |
oaire.awardNumber | IT1519-22 | en |
oaire.awardNumber | IT1443-22 | en |
oaire.awardTitle | Digital Manufacturing and Design Training Network (DiManD) | en |
oaire.awardTitle | Ingeniería de Software y Sistemas | en |
oaire.awardTitle | Mecanizado de Alto Rendimiento | en |
oaire.awardURI | https://doi.org/10.3030/814078 | en |
oaire.awardURI | Sin información | en |
oaire.awardURI | Sin información | en |
dc.unesco.clasificacion | http://skos.um.es/unesco6/120305 | en |