Network theory and CAD collections
dc.contributor.advisor
Mill, Frank
en
dc.contributor.advisor
Kamenev, Konstantin
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dc.contributor.author
Anderson, Esmé Frances Louise
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dc.contributor.sponsor
Engineering and Physical Sciences Research Council (EPSRC)
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dc.date.accessioned
2017-11-06T16:03:01Z
dc.date.available
2017-11-06T16:03:01Z
dc.date.issued
2016-11-29
dc.description.abstract
Graph and network theory have become commonplace in modern life. So widespread
in fact that most people not only understand the basics of what a network is, but are
adept at using them and do so daily. This has not long been the case however and the
relatively quick growth and uptake of network technology has sparked the interest of
many scientists and researchers. The Science of Networks has sprung up, showing how
networks are useful in connecting molecules and particles, computers and web pages, as
well as people. Despite being shown to be effective in many areas, network theory has
yet to be applied to mechanical engineering design.
This work makes use of network science advances and explores how they can impact
Computer Aided Design (CAD) data. CAD data is considered the most valuable design
data within mechanical engineering and two places large collections are found are
educational institutes and industry. This work begins by exploring 5 novel networks of
different sized CAD collections, where metrics and network developments are assessed.
From there collections from educational and industrial settings are explored in depth,
with novel methods and visualisations being presented.
The results of this investigation show that network science provides interesting analysis
of CAD collections and two key discoveries are presented: network metrics and
visualisations are shown to be effective at highlighting plagiarism in collections of students'
CAD submissions. Also when used to assess collections of real world company
data, network theory is shown to provide unique metrics for analysis and characterising
collections of CAD and associated data.
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dc.identifier.uri
http://hdl.handle.net/1842/25425
dc.language.iso
en
dc.publisher
The University of Edinburgh
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dc.relation.hasversion
Mill, F., Sherlock, A., Pan, Q., and Anderson, E. Recognising 3D products and sourcing part documentation with scanned data. Computers in Industry 64, 9 (dec 2013), 1201-1208.
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dc.relation.hasversion
Anderson, E., and Mill, F. Detection of design reuse in 3D CAD using network analysis. In 6th International Integrity and Plagiarism Conference (Newcastle, 2014), Plagiarismadvice.org.
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dc.subject
network
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dc.subject
CAD
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dc.subject
plagiarism
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dc.title
Network theory and CAD collections
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Doctoral
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dc.type.qualificationname
PhD Doctor of Philosophy
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