Wear curve based online feature assessment for tool condition monitoring

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dc.identifier.uri http://dx.doi.org/10.15488/10663
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/10741
dc.contributor.author Denkena, Berend
dc.contributor.author Bergmann, Benjamin
dc.contributor.author Stiehl, Tobias H.
dc.date.accessioned 2021-03-26T10:06:24Z
dc.date.available 2021-03-26T10:06:24Z
dc.date.issued 2020
dc.identifier.citation Denkena, B.; Bergmann, B.; Stiehl, T.H.: Wear curve based online feature assessment for tool condition monitoring. In: Procedia CIRP 88 (2020), S. 312-317. DOI: https://doi.org/10.1016/j.procir.2020.05.054
dc.description.abstract The performance of a process monitoring system is determined by the information available to it. Existing methods for selecting relevant process information (features) work offline with data of faulty processes that is often unavailable or neglect random disturbances. This increases the risk of choosing non-sensitive features. Hence, this paper investigates whether a non-sensitive feature is detectable online in an initial selection of features presumed to be sensitive. A method for quantifying and assessing trends in features online is described. In the validation with turning and drilling processes, a single non-sensitive feature was detected successfully in seven out of eight test cases. © 2020 The Authors. eng
dc.language.iso eng
dc.publisher Amsterdam : Elsevier B.V.
dc.relation.ispartofseries Procedia CIRP 88 (2020)
dc.rights CC BY-NC-ND 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Feature selection eng
dc.subject Online eng
dc.subject Tool condition monitoring eng
dc.subject Condition monitoring eng
dc.subject Intelligent computing eng
dc.subject Manufacture eng
dc.subject Process monitoring eng
dc.subject Wear of materials eng
dc.subject Drilling process eng
dc.subject Faulty process eng
dc.subject Process information eng
dc.subject Process monitoring system eng
dc.subject Random disturbances eng
dc.subject Sensitive features eng
dc.subject Tool condition monitoring eng
dc.subject Wear curves eng
dc.subject Feature extraction eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 600 | Technik ger
dc.subject.ddc 670 | Industrielle und handwerkliche Fertigung ger
dc.title Wear curve based online feature assessment for tool condition monitoring
dc.type Article
dc.type Text
dc.relation.essn 2212-8271
dc.relation.doi https://doi.org/10.1016/j.procir.2020.05.054
dc.bibliographicCitation.volume 88
dc.bibliographicCitation.firstPage 312
dc.bibliographicCitation.lastPage 317
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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