Turning: Autonomous process set-up through Bayesian optimization and Gaussian process models
dc.contributor.author
Maier, Markus
dc.contributor.author
Rupenyan-Vasileva, Alisa Bohos
dc.contributor.author
Akbari, Mansur
dc.contributor.author
Zwicker, Ruben
dc.contributor.author
Wegener, Konrad
dc.contributor.editor
Teti, Roberto
dc.contributor.editor
D'Addona, Doriana M.
dc.date.accessioned
2020-06-15T09:49:56Z
dc.date.available
2019-12-17T09:42:36Z
dc.date.available
2020-06-02T12:28:44Z
dc.date.available
2020-06-03T05:26:02Z
dc.date.available
2020-06-15T09:49:56Z
dc.date.issued
2020
dc.identifier.issn
2212-8271
dc.identifier.other
10.1016/j.procir.2020.05.053
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/386017
dc.identifier.doi
10.3929/ethz-b-000386017
dc.description.abstract
In turning, process parameters such as feed rate and cutting speed are normally selected by an operator and adapted depending on the process requirements. In this study, autonomous process set-up of the turning process is investigated based on Bayesian optimization and Gaussian process models. Unconstrained Bayesian optimization is compared to an implementation with added constraint with simulated machining parameters. It is shown that constraint Bayesian optimization has a better performance in the turning process set-up. The method is suitable for measurements with uncertainty and typically results in reduced number of experiments.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Bayesian optimization
en_US
dc.subject
Process set-up
en_US
dc.subject
Turning
en_US
dc.subject
Gaussian process models
en_US
dc.title
Turning: Autonomous process set-up through Bayesian optimization and Gaussian process models
en_US
dc.type
Conference Paper
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2020-06-13
ethz.journal.title
Procedia CRIP
ethz.journal.volume
88
en_US
ethz.pages.start
306
en_US
ethz.pages.end
311
en_US
ethz.size
6 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.event
13th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ˈ19)
en_US
ethz.event.location
Gulf of Naples, Italy
en_US
ethz.event.date
July 17-19, 2019
en_US
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 (emeritus) / Wegener, Konrad (emeritus)
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 (emeritus) / Wegener, Konrad (emeritus)
en_US
ethz.date.deposited
2019-12-17T09:42:47Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-06-15T09:50:12Z
ethz.rosetta.lastUpdated
2024-02-02T11:08:24Z
ethz.rosetta.versionExported
true
ethz.COinS
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