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dc.contributor.authorDuo, Aitor
dc.contributor.authorReguera-Bakhache, Daniel
dc.contributor.authorIzagirre, Unai
dc.contributor.authorAperribay Zubia, Javier
dc.date.accessioned2022-11-23T10:25:43Z
dc.date.available2022-11-23T10:25:43Z
dc.date.issued2022
dc.identifier.isbn978-1-6654-9996-5en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=168355en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5879
dc.description.abstractIn the context of Industry 4.0, the optimization of manufacturing processes is a challenge. Although in recent years many of the efforts have been in this direction, there is still improvement opportunities in these processes. The optimisation of the power consumed by the processes can be improved by means of the parameters of control. To date, this challenge has been addressed by Multi-Objective optimization techniques, however, Reinforcement Learning based approaches are raising with promising results in many industrial fields.In this paper, we propose a Reinforcement Learning (RL) based approach to optimize the active power consumption of a machining process by the cutting conditions selection. Through the application of Q-Learning algorithm, the agent self-learns the optimal solution through interacting with the environment. The approach was validated in three different scenarios demonstrating the feasibility of RL application to determine the cutting conditions values in order to optimize the active power consumption.en
dc.description.sponsorshipGobierno Vasco-Eusko Jaurlaritzaes
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2022 IEEEen
dc.subjectQ-learningen
dc.subjectPower demanden
dc.subjectManufacturing processesen
dc.subjectProcess controlen
dc.subjectTurningen
dc.subjectFourth Industrial Revolutionen
dc.subjectoptimizationen
dc.titleActive Power Optimization of a Turning Process by Cutting Conditions Selection: A Q-Learning Approachen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.source2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)en
local.contributor.groupAnálisis de datos y ciberseguridades
local.contributor.groupMecanizado de alto rendimientoes
local.description.peerreviewedtrueen
local.description.publicationfirstpage1en
local.description.publicationlastpage6en
local.identifier.doihttps://doi.org/10.1109/ETFA52439.2022.9921714en
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2022-2025/IT1676-22/CAPV/Grupo de sistemas inteligentes para sistemas industriales/en
local.embargo.enddate2024-10-31
local.source.details6-9 septiembre, Stuttgart. Pp. 1-6en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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