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dc.rights.licenseAttribution 4.0 International
dc.contributor.authorDuo, Aitor
dc.contributor.authorBasagoiti, Rosa
dc.contributor.authorARRAZOLA, PEDRO JOSE
dc.contributor.otherSegreto, Tiziana
dc.contributor.otherCaggiano, Alessandra
dc.contributor.otherTeti, Roberto
dc.description.sponsorshipComisión Europea
dc.description.sponsorshipGobierno de España
dc.description.sponsorshipGobierno Vasco
dc.publisherElsevier B.V.
dc.rights© 2021 The Authors
dc.titleDrilling process monitoring: A framework for data gathering and feature extraction techniques
dcterms.abstractToday’s industrial transformation is taking advantage of the benefits of information and communication technologies (ICT) to evolve into a more decision-making environment in manufacturing. Efficiency, agility, innovation, quality and cost savings are the goals of this transformation in one of the most employed manufacturing processes as is the case of machining. Drilling processes are among the last operations in the different manufacturing stages of machined parts, where an undetected problem can lead to the production of a defective part. Data analysis of sensor signals gathered during drilling processes provides information related to the cutting process that can anticipate non-desired phenomena. This work illustrates the experimental setup for sensorial data acquisition in drilling processes, signal processing techniques and feature extraction methodologies for faster and more robust decision-making paradigms.
dcterms.sourceProcedia CIRP
dcterms.subjectSignal processing
dcterms.subjectFeature extraction
dcterms.subjectFeature selection
dcterms.typeConference Paper
local.contributor.groupMecanizado de alto rendimiento
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/737459/EU/Electronics and ICT as enabler for digital industry and optimized supply chain management covering the entire product lifecycle/PRODUCTIVE4.0
local.relation.projectIDinfo:eu-repo/grantAgreement/GE/Acciones de programación conjunta internacional, del programa Estatal de investigación, desarrollo e innovación orientada a los retos de la sociedad, del plan estatal de investigación científica y técnica y de innovación 2013-2016, convocatoria 2017/PCIN-2017-071/ES/Electronica y TICs para facilitar la industria digital y optimizar la gestion de la cadena de Suministro cubriendo todo el ciclo de vida del producto/PRODUCTIVE 4.0
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Elkartek 2020/KK-2020-00103/CAPV/Herramientas de corte inteligentes sensorizadas mediante recubrimientos funcionales/INTOOLII
local.contributor.otherinstitutionUniversità degli Studi di Napoli Federico II
local.source.detailsVol. 99. Pp. 189-195, 2021

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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International