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dc.contributor.authorDuo, Aitor
dc.contributor.authorBasagoiti, Rosa
dc.contributor.authorARRAZOLA, PEDRO JOSE
dc.contributor.authorCUESTA ZABALAJAUREGUI, MIKEL
dc.contributor.authorIllarramendi, Miren
dc.date.accessioned2022-07-05T13:14:35Z
dc.date.available2022-07-05T13:14:35Z
dc.date.issued2022
dc.identifier.issn1755-5817en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=166857en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5622
dc.description.abstractDrilling is a continuous cutting process where two or more cutting edges remove the material, to obtain the desired feature. During the chip evacuation, it generally rubs against the generated surface. Thus, the roughness obtained differs from other machining processes such as turning or milling. Therefore, surface roughness can be different from the analytically expected one. In this research work, an analysis of the cutting conditions where a level of roughness is expected to meet specific requirements has been carried out. 600 holes were made with two different tool geometries on steel without modifying the cutting conditions. When analysing the surface generated, certain variability in the roughness profiles obtained can be observed. External signals to the machine tool were acquired with sensors (cutting forces, vibrations, and acoustic emissions) as well as internal signals (spindle power, spindle torque in the Z-axis, spindle current and positions, speeds, accelerations, and jerk of the tool tip in the three axes of the machine). The most representative statistical features of the signals regarding roughness were selected using correlation analysis. Besides that, the hierarchical clustering of statistical features of the external and internal signals of the process was compared with clusters obtained using roughness parameters. Results show that clusters appear using signals highly related to the roughness parameters obtained from the measured profiles, confirming a mapping between the acquired signals during the machining process and the roughness of the holes.en
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2021 CIRPen
dc.subjectDrillingen
dc.subjectSurface Roughnesen
dc.subjectClustering, PCAen
dc.titleSurface roughness assessment on hole drilled through the identification and clustering of relevant external and internal signal statistical featuresen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/embargoedAccessen
dcterms.sourceCIRP Journal of manufacturing science and technologyen
dc.description.versioninfo:eu-repo/semantics/acceptedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.contributor.groupMecanizado de alto rendimientoes
local.description.peerreviewedtrueen
local.description.publicationfirstpage143en
local.description.publicationlastpage157en
local.identifier.doihttps://doi.org/10.1016/j.cirpj.2021.11.007en
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.detailsVol. 36. Pp. 143-157, 2022en


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