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A Data Clustering Approach for Automated Optical Inspection of Metal Work Pieces

  • This paper describes the use of the single-linkage hierarchical clustering method in outlier detection for manufactured metal work pieces. The main goal of the study is to group defects that occur 5 mm into a work piece from the edge, i.e., the border of the metal work piece. The goal is to remove defects outside the area of interest as outliers. According to the assumptions made for theThis paper describes the use of the single-linkage hierarchical clustering method in outlier detection for manufactured metal work pieces. The main goal of the study is to group defects that occur 5 mm into a work piece from the edge, i.e., the border of the metal work piece. The goal is to remove defects outside the area of interest as outliers. According to the assumptions made for the performance criteria, the single-linkage method has achieved better results compared to other agglomeration methods.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/3108
Bibliografische Angaben
Title (English):A Data Clustering Approach for Automated Optical Inspection of Metal Work Pieces
Conference:International Conference on Big Data, Small Data, Linked Data and Open Data (4. : April 22-26, 2018 : Athens, Greece)
Author:Stephan TrahaschStaff MemberORCiDGND, Tobias LauerStaff MemberGND, Ruth Zibello
Year of Publication:2018
First Page:64
Last Page:68
Parent Title (English):ALLDATA 2018, The Fourth International Conference on Big Data, Small Data, Linked Data and Open Data
ISBN:978-1-61208-631-6
ISSN:2519-8386
URL:https://www.thinkmind.org/index.php?view=article&articleid=alldata_2018_5_10_80060
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019)
Institutes:Bibliografie
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoUrheberrechtlich geschützt