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dc.contributor.author
Gittler, Thomas
dc.contributor.author
Glasder, Magnus
dc.contributor.author
Öztürk, Elif
dc.contributor.author
Lüthi, Michel
dc.contributor.author
Weiss, Lukas
dc.contributor.author
Wegener, Konrad
dc.date.accessioned
2021-11-09T09:34:38Z
dc.date.available
2021-07-15T10:24:29Z
dc.date.available
2021-07-22T15:44:54Z
dc.date.available
2021-11-09T09:34:38Z
dc.date.issued
2021-12
dc.identifier.issn
0268-3768
dc.identifier.issn
1433-3015
dc.identifier.other
10.1007/s00170-021-07281-2
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/494836
dc.identifier.doi
10.3929/ethz-b-000494836
dc.description.abstract
Degraded or defect machine components and consumables negatively impact manufacturing quality and productivity. Diagnosing and predicting the wear or degradation status of critical machine components or parts are therefore of general interest. To tackle this challenge, data-driven approaches based on supervised machine learning principles have demonstrated promising results. However, supervised learning models capable of degradation identification require large quantities of data. In practice, run-to-failure data in large amounts is usually not available and expensive to obtain. To overcome this issue, this study proposes an unsupervised learning approach for degradation prognostics of machine tool components and consumables. It uses time series of multi-sensor signal data, which are transformed into a feature representation. The features consist of various characterizations of the time series, allowing to make different signal measurements comparable, and cluster them according to their feature values. The herewith obtained density-based clustering model is used to diagnose and predict the degradation states of components and parts in unknown conditions. The novelty in the proposed approach lies within the identification of continuous component and part degradation states based on unsupervised learning principles. The proposal is verified and demonstrated on an exemplary data set containing a small sample of run-to-failure multi-sensor signals of milling inserts and their corresponding wear state. By the application of the proposed procedure on the exemplary data set, we demonstrate that an unsupervised clustering approach is capable of separating wear data such that meaningful and accurate estimations of the part condition are possible. The advantages are its ability to cope with scarce data sets, its limited engineering and hyperparameter tuning effort, and its straightforward implementation to a multitude of degradation and wear diagnostics scenarios.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Condition monitoring
en_US
dc.subject
Machine learning
en_US
dc.subject
Prognostics and health monitoring
en_US
dc.subject
Unsupervised learning
en_US
dc.subject
Machine tools
en_US
dc.subject
Manufacturing
en_US
dc.subject
Milling
en_US
dc.subject
Tool wear
en_US
dc.title
International Conference on Advanced and Competitive Manufacturing Technologies milling tool wear prediction using unsupervised machine learning
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-05-24
ethz.journal.title
The International Journal of Advanced Manufacturing Technology
ethz.journal.volume
117
en_US
ethz.journal.issue
7
en_US
ethz.journal.abbreviated
Int J Adv Manuf Technol
ethz.pages.start
2213
en_US
ethz.pages.end
2226
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02623 - Inst. f. Werkzeugmaschinen und Fertigung / Inst. Machine Tools and Manufacturing::03641 - Wegener, Konrad (emeritus) / Wegener, Konrad (emeritus)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02623 - Inst. f. Werkzeugmaschinen und Fertigung / Inst. Machine Tools and Manufacturing::03641 - Wegener, Konrad (emeritus) / Wegener, Konrad (emeritus)
ethz.date.deposited
2021-07-15T10:25:50Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-11-09T09:34:46Z
ethz.rosetta.lastUpdated
2024-02-02T15:20:10Z
ethz.rosetta.versionExported
true
ethz.COinS
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