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dc.contributor.author
Stoop, Fabian
dc.contributor.author
Mayr, Josef
dc.contributor.author
Sulz, Clemens
dc.contributor.author
Bleicher, Friedrich
dc.contributor.author
Wegener, Konrad
dc.date.accessioned
2022-01-13T13:49:14Z
dc.date.available
2021-11-16T17:40:00Z
dc.date.available
2021-11-17T08:41:21Z
dc.date.available
2021-11-17T08:43:23Z
dc.date.available
2022-01-13T13:49:14Z
dc.date.issued
2021
dc.identifier.isbn
978-1-7281-2989-1
en_US
dc.identifier.isbn
978-1-7281-2990-7
en_US
dc.identifier.other
10.1109/ETFA45728.2021.9613231
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/515486
dc.identifier.doi
10.3929/ethz-b-000515486
dc.description.abstract
Thermal error compensation of machine tools promotes sustainable production. The thermal adaptive learning control (TALC) and machine learning approaches are the required enabling principals. Fleet learnings are key resources to develop sustainable machine tool fleets in terms of thermally induced machine tool error. The target is to integrate each machine tool of the fleet in a learning network. Federated learning with a central cloud server and dedicated edge computing on the one hand keeps the independence of each individual machine tool high and on the other hand leverages the learning of the entire fleet. The outlined concept is based on the TALC, combined with a machine agnostic and machine specific characterization and communication. The proposed system is validated with environmental measurements for two machine tools of the same type, one situated at ETH Zurich and the other one at TU Wien.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
fleet learning
en_US
dc.subject
Machine tool
en_US
dc.subject
Industry 4.0
en_US
dc.subject
thermal error compensation
en_US
dc.subject
Federated learning
en_US
dc.title
Fleet learning of thermal error compensation in machine tools
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-11-30
ethz.book.title
2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )
en_US
ethz.pages.start
1
en_US
ethz.pages.end
4
en_US
ethz.size
4 p.
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
26th International Conference on Emerging Technologies and Factory Automation (ETFA 2021)
en_US
ethz.event.location
Vasteras, Sweden
en_US
ethz.event.date
September 7–10, 2021
en_US
ethz.notes
Conference lecture held on September 9, 2021.
en_US
ethz.identifier.wos
ethz.publication.place
Piscataway, NJ
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
2021-11-16T17:40:18Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-01-13T13:49:22Z
ethz.rosetta.lastUpdated
2022-01-13T13:49:22Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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