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Data augmentation based surface quality monitoring with machine learning models
KTH, School of Industrial Engineering and Management (ITM), Production engineering, Manufacturing and Metrology Systems.ORCID iD: 0000-0002-1151-5769
2023 (English)Conference paper, Oral presentation only (Other academic)
Place, publisher, year, edition, pages
2023.
Keywords [en]
Data augmentation, surface quality monitoring, machine learning
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-352474OAI: oai:DiVA.org:kth-352474DiVA, id: diva2:1894316
Conference
Manufacturing R&D Cluster Conference, Södertälje, May 9-10, 2023
Note

QC 20240903

Available from: 2024-09-03 Created: 2024-09-03 Last updated: 2024-09-03Bibliographically approved

Open Access in DiVA

fulltext(1932 kB)168 downloads
File information
File name FULLTEXT01.pdfFile size 1932 kBChecksum SHA-512
47a4801255e2e9b30f17f08b3f4483a86e180dddafc9c15e21108a26f0909022dcba3182bb6ff40f57e992204b0d80f54c25a578efe03c5c23fccd3849068574
Type fulltextMimetype application/pdf

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Zhu, Yaoxuan

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Zhu, Yaoxuan
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Manufacturing and Metrology Systems
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Total: 171 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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