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dc.contributor.authorDuo, Aitor
dc.contributor.authorBasagoiti, Rosa
dc.contributor.authorARRAZOLA, PEDRO JOSE
dc.contributor.authorCUESTA ZABALAJAUREGUI, MIKEL
dc.date.accessioned2022-07-05T13:05:18Z
dc.date.available2022-07-05T13:05:18Z
dc.date.issued2021
dc.identifier.issn1362-3052en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=166262en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5621
dc.description.abstractTool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool wear is reached can avoid costs resulting from that undesirable situation. This research work presents the results obtained in drilling process monitoring carried out on Inconel 718. Monitoring systems should be light and scalable. Following this idea, multiple sensors for external signal acquisition are used in this work (cutting forces, vibrations, and acoustic emissions) and several machine internal signals are collected. The main objective is to evaluate the capacity of each acquisition source for the reconstruction of the tool wear curve and subsequently detection of tool breakage. Given the difficulty of using all of these signals in a real system, the methodology used to analyse the data makes it possible to have a comparative analysis of the potential of each of these sources for tool wear monitoring during the drilling process. The results indicate cutting forces whether they come from internal signals or external signals can carry out this task accurately. At the same time of data acquisition, detailed tool wear measurements were added.en
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherTaylor & Francisen
dc.rights© 2021 Taylor & Francisen
dc.subjectTool condition monitoringen
dc.subjectInconel 718en
dc.subjectDrillingen
dc.subjectData miningen
dc.subjectMachine learningen
dc.titleSensor signal selection for tool wear curve estimation and subsequent tool breakage prediction in a drilling operationen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/embargoedAccessen
dcterms.sourceInternational Journal of Computer Integrated Manufacturingen
dc.description.versioninfo:eu-repo/semantics/acceptedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.contributor.groupMecanizado de alto rendimientoes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1080/0951192X.2021.1992661en
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Elkartek 2020/KK-2020-00103/CAPV/Herramientas de corte inteligentes sensorizadas mediante recubrimientos funcionales/INTOOL IIen
local.source.detailsVolume 35, Issue 2, 2022en


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