<div class="csl-bib-body">
<div class="csl-entry">Stojadinovic, S. M., Majstorovic, V. D., & Durakbasa, N. M. (2020). Toward a cyber-physical manufacturing metrology model for industry 4.0. <i>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</i>, <i>35</i>(1), 20–36. https://doi.org/10.1017/s0890060420000347</div>
</div>
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dc.identifier.issn
0890-0604
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/140807
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dc.description.abstract
Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, highproductive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.
en
dc.language.iso
en
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dc.publisher
CAMBRIDGE UNIV PRESS
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dc.relation.ispartof
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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dc.subject
Artificial Intelligence
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dc.subject
Industrial and Manufacturing Engineering
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dc.title
Toward a cyber-physical manufacturing metrology model for industry 4.0
en
dc.type
Artikel
de
dc.type
Article
en
dc.description.startpage
20
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dc.description.endpage
36
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dc.type.category
Original Research Article
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tuw.container.volume
35
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tuw.container.issue
1
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tuw.journal.peerreviewed
true
-
tuw.peerreviewed
true
-
tuw.researchTopic.id
M2
-
tuw.researchTopic.id
I3
-
tuw.researchTopic.id
I6a
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tuw.researchTopic.name
Materials Characterization
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tuw.researchTopic.name
Automation and Robotics
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tuw.researchTopic.name
Digital Transformation in Manufacturing
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tuw.researchTopic.value
30
-
tuw.researchTopic.value
20
-
tuw.researchTopic.value
50
-
dcterms.isPartOf.title
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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tuw.publication.orgunit
E311-01-4 - Forschungsgruppe Fertigungsmesstechnik und adaptronische Systeme
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tuw.publisher.doi
10.1017/s0890060420000347
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dc.identifier.eissn
1469-1760
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dc.description.numberOfPages
17
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tuw.author.orcid
0000-0002-4055-5874
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wb.sci
true
-
wb.sciencebranch
Maschinenbau
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wb.sciencebranch.oefos
2030
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wb.facultyfocus
Werkstoff- und Fertigungstechnologien
de
wb.facultyfocus
Material and Production Technology
en
wb.facultyfocus.faculty
E300
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_2df8fbb1
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item.languageiso639-1
en
-
item.openairetype
research article
-
item.grantfulltext
none
-
crisitem.author.dept
E311 - Institut für Fertigungstechnik und Photonische Technologien
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crisitem.author.orcid
0000-0002-2048-1978
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crisitem.author.parentorg
E300 - Fakultät für Maschinenwesen und Betriebswissenschaften