Automatic particle classification through deep learning approaches for increasing productivity in the technical cleanliness laboratory

Zwinkau, R; Frentrup, S; Möhle, R; Deuse, J

Automatic particle classification through deep learning approaches for increasing productivity in the technical cleanliness laboratory

Zwinkau, R Frentrup, S Möhle, R Deuse, J

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Advances in Human Factors and Systems Interaction, 2020, 959, pp. 34-44
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Zwinkau, R
dc.contributor.author	Frentrup, S
dc.contributor.author	Möhle, R
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.date	2019-07-24
dc.date.accessioned	2021-05-27T08:50:23Z
dc.date.available	2021-05-27T08:50:23Z
dc.date.issued	2020-01-01
dc.identifier.citation	Advances in Human Factors and Systems Interaction, 2020, 959, pp. 34-44
dc.identifier.isbn	9783030200398
dc.identifier.issn	2194-5357
dc.identifier.issn	2194-5365
dc.identifier.uri	http://hdl.handle.net/10453/149281
dc.description.abstract	Understanding the properties of particles plays a vital role in assessing the component cleanliness and its origin in the manufacturing process. We propose a classification method using deep convolutional neural networks. Using a dataset of 70,000 annotated images, we achieve a accuracy of 97.7% for a binary classification in metal and non-metal particles comparable to state-of-the-art polarized light microscopy according to VDA 19-1 and ISO 16232. Manual follow-up checks in a cleanliness laboratory are not required due to the robustness of the classification system.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advances in Human Factors and Systems Interaction
dc.relation.ispartof	International Conference on Human Factors and Systems Interaction
dc.relation.ispartofseries	Advances in Intelligent Systems and Computing
dc.relation.isbasedon	10.1007/978-3-030-20040-4_4
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Automatic particle classification through deep learning approaches for increasing productivity in the technical cleanliness laboratory
dc.type	Conference Proceeding
utslib.citation.volume	959
utslib.location.activity	Washington D.C., USA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-27T08:50:22Z
pubs.finish-date	2019-07-28
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2019-07-24
pubs.volume	959
dc.location	Switzerland

Abstract:

Understanding the properties of particles plays a vital role in assessing the component cleanliness and its origin in the manufacturing process. We propose a classification method using deep convolutional neural networks. Using a dataset of 70,000 annotated images, we achieve a accuracy of 97.7% for a binary classification in metal and non-metal particles comparable to state-of-the-art polarized light microscopy according to VDA 19-1 and ISO 16232. Manual follow-up checks in a cleanliness laboratory are not required due to the robustness of the classification system.

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http://hdl.handle.net/10453/149281