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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorARRAZOLA, PEDRO JOSE
dc.contributor.authorAristimuño, Patxi Xabier
dc.contributor.authorSaez de Buruaga, Mikel
dc.contributor.otherZheng, Xiaochen
dc.contributor.otherPerez, Roberto
dc.contributor.otherEchebarria, Daniel
dc.contributor.otherKiritsis, Dimitris
dc.date.accessioned2023-03-28T18:37:09Z
dc.date.available2023-03-28T18:37:09Z
dc.date.issued2023
dc.identifier.issn1096-1216en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=171949en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6069
dc.description.abstractChatter is a harmful self-excited vibration that commonly occurs during milling processes. Data-driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are based on external sensors, such as accelerometers and microphones, which require installation of extra devices. Some recent studies have proved the feasibility of online chatter detection using internal signals such as drive motor current. This study aims to investigate the effectiveness of different internal signals extracted from CNC system for chatter detection and compare them with external acceleration signals. The external and internal signals are first compared with time–frequency analysis using Discrete Fourier Transform and Ensemble Empirical Mode Decomposition approaches. Two chatter detection methods are then presented based on manually and automatically extracted features respectively. The first method uses two nonlinear dimensionless indicators, C0 complexity and Power Spectral Entropy, of filtered signals. The second approach uses autoencoder for automatic feature extraction and Support Vector Machine as classifier for chatter identification. A series of milling experiments are conducted and chatters are intentionally created by changing the milling process parameters. Multiple internal signals are collected using software provided by the machine manufacturer. Results show that several internal CNC signals, such as the nominal current signal and the actual torque signal, can achieve comparable performance to external signals for chatter detection.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2023 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectChatter detectionen
dc.subjectMilling processen
dc.subjectInternal signalen
dc.subjectEmpirical mode decompositionen
dc.subjectAutoencoderen
dc.titleExploring the effectiveness of using internal CNC system signals for chatter detection in milling processen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceMechanical Systems and Signal Processingen
local.contributor.groupMecanizado de alto rendimientoes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.ymssp.2022.109812en
local.contributor.otherinstitutionhttps://ror.org/02s376052en
local.contributor.otherinstitutionGF Machining Solutionsen
local.contributor.otherinstitutionhttps://ror.org/04p0rqr57en
local.source.detailsVol. 185. Artículo 109812en
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Registro sencillo

Attribution 4.0 International
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