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Robust estimation of optimal sample size for CMM measurements with statistical tolerance limits

dc.contributor.authorChelishchev, Petr
dc.contributor.authorPopov, Aleksandr
dc.contributor.authorSørby, Knut
dc.date.accessioned2019-02-21T08:00:08Z
dc.date.available2019-02-21T08:00:08Z
dc.date.created2018-06-07T15:04:52Z
dc.date.issued2018
dc.identifier.citationMATEC Web of Conferences. 2018, 220 .nb_NO
dc.identifier.issn2261-236X
dc.identifier.urihttp://hdl.handle.net/11250/2586621
dc.description.abstractThe paper proposes the kernel probability density function approach to estimate the distribution of measurements on a part which is measured in a coordinate measuring machine (CMM). The study is based on the experimental data derived from internal cylinder measurements. The distribution free model suggested by Wilks was used as a reference for the selection of the sample size. Three cross sections of a cylinder were measured regarding to this reference. The work defines the minimum required sample size for obtaining at least 0.95 proportion of radius variation for particular studied cylindrical part with 95% confidence level.nb_NO
dc.language.isoengnb_NO
dc.publisherEDP Sciencesnb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleRobust estimation of optimal sample size for CMM measurements with statistical tolerance limitsnb_NO
dc.title.alternativeRobust estimation of optimal sample size for CMM measurements with statistical tolerance limitsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber5nb_NO
dc.source.volume220nb_NO
dc.source.journalMATEC Web of Conferencesnb_NO
dc.identifier.doi10.1051/matecconf/201822004001
dc.identifier.cristin1589819
dc.description.localcode© The Authors, published by EDP Sciences, 2018. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citednb_NO
cristin.unitcode194,64,92,0
cristin.unitnameInstitutt for maskinteknikk og produksjon
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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