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Data Analytics for Manufacturing Systems – A Data-Driven Approach for Process Optimization

Ungermann, Florian 1; Kuhnle, Andreas 1; Stricker, Nicole 1; Lanza, Gisela 1
1 Karlsruher Institut für Technologie (KIT)

Abstract:

In the course of digitalization many small and medium-sized companies face the challenge of using the existing database for process optimization in manufacturing. Furthermore, the demand-oriented expansion of the database is a great challenge. A lack of competencies, limited financial resources and historically grown data structures, which show a strong heterogeneity and lack of transparency, are the central obstacles. A specific approach, how data analytics projects for process optimization should be carried out in manufacturing, is presented. In particular, the question which sensors should be implemented to expand the database is answered. The approach is applied exemplarily for a manufacturing line.


Verlagsausgabe §
DOI: 10.5445/IR/1000145184
Veröffentlicht am 06.07.2022
Originalveröffentlichung
DOI: 10.1016/j.procir.2019.03.064
Scopus
Zitationen: 20
Dimensions
Zitationen: 18
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000145184
Erschienen in Procedia CIRP
Verlag Elsevier
Band 81
Seiten 369–374
Bemerkung zur Veröffentlichung 52nd CIRP Conference on Manufacturing Systems (CMS), Ljubljana, Slovenia, June 12-14, 2019
Vorab online veröffentlicht am 24.06.2019
Nachgewiesen in Dimensions
Scopus
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