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dc.contributor.author
Zimmermann, Nico
dc.contributor.author
Lang, Sebastian
dc.contributor.author
Blaser, Philip
dc.contributor.author
Mayr, Josef
dc.date.accessioned
2020-10-15T11:26:37Z
dc.date.available
2020-10-14T07:48:03Z
dc.date.available
2020-10-15T11:26:37Z
dc.date.issued
2020
dc.identifier.issn
0007-8506
dc.identifier.issn
1660-2773
dc.identifier.other
10.1016/j.cirp.2020.03.017
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/445884
dc.description.abstract
The presented method selects optimal inputs for compensation models based on the Thermal Adaptive Learning Control methodology. The number of inputs and the individual inputs for each considered thermal error are automatically adapted. The intelligent combination of k-means clustering and Time Series Cluster Kernel enables the approach to handle time series of thermal error measurements with missing data due to operational reasons. The results show that the adaptive sensor selection approach, tested on a 5-axis-machine tool, significantly increases the robustness of the used compensation model. The productivity loss due to on-machine measurements is reduced by approximately 40 percent. © 2020 CIRP. Published by Elsevier Ltd. All rights reserved.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Adaptive manufacturing
en_US
dc.subject
Thermal error
en_US
dc.subject
Sensor selection
en_US
dc.title
Adaptive input selection for thermal error compensation models
en_US
dc.type
Journal Article
dc.date.published
2020-05-17
ethz.journal.title
CIRP Annals
ethz.journal.volume
69
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
CIRP Ann.
ethz.pages.start
485
en_US
ethz.pages.end
488
en_US
ethz.identifier.wos
ethz.publication.place
Amsterdam
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 / Wegener, Konrad
en_US
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 / Wegener, Konrad
en_US
ethz.date.deposited
2020-10-14T07:48:12Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-10-15T11:26:49Z
ethz.rosetta.lastUpdated
2021-02-15T18:23:48Z
ethz.rosetta.versionExported
true
ethz.COinS
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