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A data-driven approach for quality analytics of screwing processes in a global learning factory

Yang, S.; Liu, H.; Zhang, Y.; Arndt, T.; Hofmann, C.; Häfner, B.; Lanza, G.

Quality problems of screwing processes in assembly systems, which are an important issue for operation excellence, needs to be quickly analyzed and solved. A network can be very beneficial for root cause analysis due to different data from various factories. Nevertheless, it is difficult to obtain reliable and consistent data. In this context, this paper aims to develop a method for data-driven oriented quality analytics of screwing processes considering a global production network. Firstly, the overview of data structure is introduced. Further, the data transformation is modelled for edge- and cloud-based analytics across the global production network. Lastly, the rules for analyzing are identified. A joint case study based on Learning Factory Global Production (LF) in Germany and I4.0 Innovation Centre and Artificial Intelligence Innovation Factory (IC&AIIF) in China is used to validate the proposed approach, which is also a new teaching method for quality analysis in the framework of learning factory.

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Verlagsausgabe §
DOI: 10.5445/IR/1000120495
Veröffentlicht am 24.06.2020
DOI: 10.1016/j.promfg.2020.04.052
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2351-9789
KITopen-ID: 1000120495
Erschienen in Procedia manufacturing
Band 45
Seiten 454-459
Bemerkung zur Veröffentlichung 10th Conference on Learning Factories, CLF 2020, Graz, Austria, 15 - 17 April 2020
Nachgewiesen in Scopus
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