Data Mining-based forecasting of rework duration-Predictive rework control and work process optimization in the automotive assembly

Krzoska, S; Eickelmann, M; Schmitt, J; Deuse, J

Data Mining-based forecasting of rework duration-Predictive rework control and work process optimization in the automotive assembly

Krzoska, S Eickelmann, M Schmitt, J Deuse, J

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: WT Werkstattstechnik, 2017, 107, (10), pp. 773-778
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Krzoska, S
dc.contributor.author	Eickelmann, M
dc.contributor.author	Schmitt, J
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.date.accessioned	2022-08-26T01:57:42Z
dc.date.available	2022-08-26T01:57:42Z
dc.date.issued	2017-01-01
dc.identifier.citation	WT Werkstattstechnik, 2017, 107, (10), pp. 773-778
dc.identifier.issn	1436-4980
dc.identifier.issn	1436-4980
dc.identifier.uri	http://hdl.handle.net/10453/160890
dc.description.abstract	The article shows at the example of rework control and operating process optimization in the car assembly how recorded product- and process-related quality data can be analyzed and used efficiently by using Data Mining-methods. With data from MES-systems regression trees were built for a vehicle-specific rework duration forecast. The basic concept and validation results will be presented below.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	WT Werkstattstechnik
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Data Mining-based forecasting of rework duration-Predictive rework control and work process optimization in the automotive assembly
dc.type	Journal Article
utslib.citation.volume	107
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	2022-08-26T01:57:41Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	107
utslib.citation.issue	10

Abstract:

The article shows at the example of rework control and operating process optimization in the car assembly how recorded product- and process-related quality data can be analyzed and used efficiently by using Data Mining-methods. With data from MES-systems regression trees were built for a vehicle-specific rework duration forecast. The basic concept and validation results will be presented below.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/160890