Similarity-search and Prediction Based Process Parameter Adaptation for Quality Improvement in Interlinked Manufacturing Processes

Schmitt, J; Deuse, J

Similarity-search and Prediction Based Process Parameter Adaptation for Quality Improvement in Interlinked Manufacturing Processes

Schmitt, J Deuse, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Industrial Engineering and Engineering Management, 2019, 2019-December, pp. 700-704
Issue Date:: 2019-01-09

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Field	Value	Language
dc.contributor.author	Schmitt, J
dc.contributor.author	Deuse, J
dc.date	2018-12-16
dc.date.accessioned	2020-06-17T00:36:21Z
dc.date.available	2020-06-17T00:36:21Z
dc.date.issued	2019-01-09
dc.identifier.citation	IEEE International Conference on Industrial Engineering and Engineering Management, 2019, 2019-December, pp. 700-704
dc.identifier.isbn	9781538667866
dc.identifier.issn	2157-3611
dc.identifier.issn	2157-362X
dc.identifier.uri	http://hdl.handle.net/10453/141464
dc.description.abstract	© 2018 IEEE. Due to the steadily increasing global competition, manufacturing companies are forced to constantly improve their products and processes. In this context, real-time process adaptation based on inline quality monitoring using predictive data mining techniques presents a promising approach to sustainably increase manufacturing process efficiency and improve product quality. This paper presents an approach to improve process and product quality in manufacturing through process parameter adaptations utilizing quality prediction models and similarity search algorithms. The approach enables a data-driven decision support for process control in interlinked manufacturing processes.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Industrial Engineering and Engineering Management
dc.relation.ispartof	IEEE International Conference on Industrial Engineering and Engineering Management
dc.relation.isbasedon	10.1109/IEEM.2018.8607361
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Similarity-search and Prediction Based Process Parameter Adaptation for Quality Improvement in Interlinked Manufacturing Processes
dc.type	Conference Proceeding
utslib.citation.volume	2019-December
utslib.location.activity	Thailand
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-17T00:36:16Z
pubs.finish-date	2018-12-19
pubs.publication-status	Published
pubs.start-date	2018-12-16
pubs.volume	2019-December

Abstract:

© 2018 IEEE. Due to the steadily increasing global competition, manufacturing companies are forced to constantly improve their products and processes. In this context, real-time process adaptation based on inline quality monitoring using predictive data mining techniques presents a promising approach to sustainably increase manufacturing process efficiency and improve product quality. This paper presents an approach to improve process and product quality in manufacturing through process parameter adaptations utilizing quality prediction models and similarity search algorithms. The approach enables a data-driven decision support for process control in interlinked manufacturing processes.

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http://hdl.handle.net/10453/141464