<div class="csl-bib-body">
<div class="csl-entry">Biebl, F., Glawar, R., Jalali, A., Ansari, F., Haslhofer, B., Boer, P. de, & Sihn, W. (2020). A conceptual model to enable prescriptive maintenance for etching equipment in semiconductor manufacturing. In R. Teti & D. M. D’Addona (Eds.), <i>13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 17-19 July 2019, Gulf of Naples, Italy</i> (pp. 64–69). Elsevier BV. https://doi.org/10.1016/j.procir.2020.05.012</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/140827
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dc.description.abstract
The high equipment intensity and complexity of production processes in semiconductor manufacturing leads to challenging requirements regarding plant availability in this competitive market. In the present paper, we address these challenges by proposing a conceptual model to enable prescriptive maintenance in semiconductor manufacturing. Different Machine Learning Algorithms are used to predict time-to-failure intervals for unplanned downtimes. Furthermore, the concept uses Bayesian Networks to predict the root cause of a failure and ultimately leads to recommendations, which are integrated into maintenance planning routines, in order to increase the system availability by initiating specific maintenance measures. Finally, the benefit of prescriptive maintenance is demonstrated in an industrial use case for etching equipment in semiconductor manufacturing
en
dc.language.iso
en
-
dc.relation.ispartofseries
Procedia CIRP
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dc.subject
General Materials Science
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dc.subject
Prediction Model
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dc.subject
Maintenance
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dc.subject
Semiconductor manufacturing
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dc.subject
Bayesian network
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dc.title
A conceptual model to enable prescriptive maintenance for etching equipment in semiconductor manufacturing
en
dc.type
Konferenzbeitrag
de
dc.type
Inproceedings
en
dc.relation.publication
13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 17-19 July 2019, Gulf of Naples, Italy
-
dc.description.startpage
64
-
dc.description.endpage
69
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 17-19 July 2019, Gulf of Naples, Italy
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tuw.container.volume
88
-
tuw.peerreviewed
true
-
tuw.relation.publisher
Elsevier BV
-
tuw.researchTopic.id
X1
-
tuw.researchTopic.name
außerhalb der gesamtuniversitären Forschungsschwerpunkte
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E330-02-1 - Forschungsgruppe Smart and Knowledge Based Maintenance
-
tuw.publication.orgunit
E330-02 - Forschungsbereich Betriebstechnik, Systemplanung und Facility Management
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tuw.publisher.doi
10.1016/j.procir.2020.05.012
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dc.description.numberOfPages
6
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tuw.event.name
CIRP ICME '19
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tuw.event.startdate
17-07-2019
-
tuw.event.enddate
19-07-2019
-
tuw.event.online
On Site
-
tuw.event.type
Event for scientific audience
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tuw.event.place
Neapel
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tuw.event.country
IT
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tuw.event.presenter
Biebl, Fabian
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wb.sciencebranch
Wirtschaftswissenschaften
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wb.sciencebranch.oefos
5020
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wb.facultyfocus
Produktionssysteme und Industrial Management
de
wb.facultyfocus
Produktionssysteme und Industrial Management
en
wb.facultyfocus.faculty
E300
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item.fulltext
no Fulltext
-
item.cerifentitytype
Publications
-
item.openairetype
conference paper
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item.languageiso639-1
en
-
item.grantfulltext
none
-
item.openairecristype
http://purl.org/coar/resource_type/c_5794
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crisitem.author.dept
E330 - Institut für Managementwissenschaften
-
crisitem.author.dept
E330 - Institut für Managementwissenschaften
-
crisitem.author.dept
E330 - Institut für Managementwissenschaften
-
crisitem.author.parentorg
E300 - Fakultät für Maschinenwesen und Betriebswissenschaften
-
crisitem.author.parentorg
E300 - Fakultät für Maschinenwesen und Betriebswissenschaften
-
crisitem.author.parentorg
E300 - Fakultät für Maschinenwesen und Betriebswissenschaften
