Drilling process monitoring: A framework for data gathering and feature extraction techniques

Duo, Aitor; Basagoiti, Rosa; ARRAZOLA, PEDRO JOSE

dc.rights.license	Attribution 4.0 International
dc.contributor.author	Duo, Aitor
dc.contributor.author	Basagoiti, Rosa
dc.contributor.author	ARRAZOLA, PEDRO JOSE
dc.contributor.other	Segreto, Tiziana
dc.contributor.other	Caggiano, Alessandra
dc.contributor.other	Teti, Roberto
dc.date.accessioned	2021-10-27T14:19:58Z
dc.date.available	2021-10-27T14:19:58Z
dc.date.issued	2021
dc.identifier	SCOPUS_ID:85106437146
dc.identifier.issn	2212-8271
dc.identifier.other	https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=165002
dc.identifier.uri	https://hdl.handle.net/20.500.11984/5405
dc.description.sponsorship	Comisión Europea
dc.description.sponsorship	Gobierno de España
dc.description.sponsorship	Gobierno Vasco
dc.publisher	Elsevier B.V.
dc.relation.uri	https://api.elsevier.com/content/abstract/scopus_id/85106437146
dc.rights	© 2021 The Authors
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.title	Drilling process monitoring: A framework for data gathering and feature extraction techniques
dcterms.abstract	Today’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.accessRights	http://purl.org/coar/access_right/c_abf2
dcterms.source	Procedia CIRP
dcterms.subject	Drilling
dcterms.subject	Sensors
dcterms.subject	Signal processing
dcterms.subject	Feature extraction
dcterms.subject	Feature selection
dcterms.type	Conference Paper
local.contributor.group	Mecanizado de alto rendimiento
local.description.peerreviewed	true
local.identifier.doi	10.1016/j.procir.2021.03.123
local.relation.projectID	info: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.projectID	info: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.projectID	info:eu-repo/grantAgreement/GV/Elkartek 2020/KK-2020-00103/CAPV/Herramientas de corte inteligentes sensorizadas mediante recubrimientos funcionales/INTOOLII
local.contributor.otherinstitution	https://ror.org/05290cv24
local.source.details	Vol. 99. Pp. 189-195, 2021
oaire.format.mimetype	application/pdf
oaire.file	$DSPACE\assetstore
oaire.resourceType	http://purl.org/coar/resource_type/c_c94f
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85