TUdatalib : Research data to "Digital assistance systems to implement Machine Learning in manufacturing: A systematic review"

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dc.contributor.author	Rosemeyer, Jannik
dc.contributor.author	Pinzone, Marta
dc.contributor.author	Metternich, Joachim
dc.date.accessioned	2024-10-17T14:11:11Z
dc.date.available	2024-10-17T14:11:11Z
dc.date.issued	2024
dc.identifier.uri	https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4368
dc.description	Implementing machine learning technologies in manufacturing environment relies heavily on human expertise in terms of domain and machine learning knowledge. Yet, the required machine learning knowledge is often not available in manufacturing companies. A possible solution to overcome this competence gap and let domain experts with limited machine learning programming skills build viable applications are digital assistance systems that support the implementation. At the present, there is no comprehensive overview over corresponding assistance systems. Thus, within this study a systematic literature review based on the PRISMA-P process was conducted. Twenty-nine papers were identified and analyzed in depth regarding machine learning use case, required resources and research outlook. The available data show the procedure and explain how the authors arrived at the results of the accompanying paper.	de_DE
dc.language.iso	en	de_DE
dc.rights	Creative Commons Attribution-NonCommercial 4.0
dc.rights.uri	https://creativecommons.org/licenses/by-nc/4.0/
dc.subject.classification	4.11-05 Produktionssystematik, Betriebswissenschaften, Qualitätsmanagement und Fabrikplanung	de_DE
dc.subject.ddc	650
dc.title	Research data to "Digital assistance systems to implement Machine Learning in manufacturing: A systematic review"	de_DE
dc.type	Text	de_DE
dc.description.version	1	de_DE
tud.unit	TUDa
tud.history.classification	Version=2020-2024;401-05 Produktionssystematik, Betriebswissenschaften, Qualitätsmanagement und Fabrikplanung