Tool life prediction for sustainable manufacturing

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dc.contributor.authorWang, J.en
dc.contributor.authorWang, P.en
dc.contributor.authorGao, R. X.en
dc.date.accessioned2015-11-21T01:13:24Z
dc.date.available2015-10-08T12:00:00Z
dc.date.issued2013
dc.date.submitted2015-09-25
dc.descriptionPart of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 230–234.en
dc.description.abstractPrediction of tool wear is essential to maintaining the quality and integrity of machined parts and minimizing material waste, for sustainable manufacturing. Past research has investigated deterministic models such as the Taylor tool life model and its variations for tool wear prediction. Due to the inherent stochastic nature of tool wear and varying operating conditions, the accuracy of such deterministic methods has shown to be limited. This paper presents a stochastic approach to tool wear prediction, based on the particle filter. The technique integrates physics-based tool wear model with measured data to establish a framework, by iteratively updating the tool wear model with force and vibration data measured during the machining process, following the Bayesian updating scheme. Effectiveness of the developed method is demonstrated through tool wear experiments using a ball nose tungsten carbide cutter in a CNC milling machine.en
dc.identifier.uriurn:nbn:de:kobv:83-opus4-72283
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/5023
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-4726
dc.languageEnglishen
dc.language.isoenen
dc.relation.ispartof10.14279/depositonce-3753
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/de/en
dc.subject.ddc670 Industrielle Fertigungen
dc.subject.otherBayesian updatingen
dc.subject.otherParticle filteren
dc.subject.otherSustainable manufacturingen
dc.subject.otherTool wear predictionen
dc.titleTool life prediction for sustainable manufacturingen
dc.typeConference Objecten
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.originalpublishernameUniversitätsverlag der TU Berlinen
dcterms.bibliographicCitation.originalpublisherplaceBerlinen
dcterms.bibliographicCitation.pageend234en
dcterms.bibliographicCitation.pagestart230en
dcterms.bibliographicCitation.proceedingstitleInnovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013en
tub.accessrights.dnbfree*
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme::Inst. Werkzeugmaschinen und Fabrikbetriebde
tub.affiliation.facultyFak. 5 Verkehrs- und Maschinensystemede
tub.affiliation.instituteInst. Werkzeugmaschinen und Fabrikbetriebde
tub.identifier.opus47228
tub.publisher.universityorinstitutionUniversitätsverlag der TU Berlinen
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