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dc.contributor.authorDuo, Aitor
dc.contributor.authorBasagoiti, Rosa
dc.contributor.authorARRAZOLA, PEDRO JOSE
dc.contributor.authorAperribay Zubia, Javier
dc.contributor.authorCUESTA ZABALAJAUREGUI, MIKEL
dc.date.accessioned2019-07-24T10:05:08Z
dc.date.available2019-07-24T10:05:08Z
dc.date.issued2019
dc.identifier.issn1433-3015 (Online)en
dc.identifier.issn0268-3768 (Print)en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=150122en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/1476
dc.description.abstractIndustrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes.Through this, it is intended to improve the management of resources, decision-making, production costs and productiontimes. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, itis necessary to develop light and scalable TCMS that can provide information about the tool status using the signals providedby the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First,the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of toolwear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed.Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometrywere trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. Theexperiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-establishedcutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feedforce (external) and thez-axis motor torque (internal). The models created for the fulfilment of the second objective show agreat capacity of prediction even when dealing with tools with different geometrical characteristics.en
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherSpringer Verlagen
dc.rights© Springer-Verlag London Ltd., part of Springer Nature 2019en
dc.subjecttool wearen
dc.subjectDrillingen
dc.subjectMachine learningen
dc.subjectTool condition monitoringen
dc.titleThe capacity of statistical features extracted from multiple signals to predict tool wear in the drilling processen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceThe International Journal of Advanced Manufacturing Technologyen
local.contributor.groupAnálisis de datos y ciberseguridades
local.contributor.groupMecanizado de alto rendimientoes
local.description.peerreviewedtrueen
local.description.publicationfirstpage2133en
local.description.publicationlastpage2146en
local.identifier.doihttps://doi.org/10.1007/s00170-019-03300-5en
local.relation.projectIDGV/Elkartek 2017/KK-2017-00021/CAPV/Máquinas y procesos Smart a través de la integración del conocimiento y los datos/SMAPROen
local.embargo.enddate2020-01-22
local.source.detailsVol. 102. Nº 5–8. Pp 2133–2146. June, 2019eu_ES
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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