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Article: Data-driven smart production line and its common factors

TitleData-driven smart production line and its common factors
Authors
KeywordsCommon factors
Data-driven
Energy consumption
Integration
Smart production line (SPL)
Issue Date2019
PublisherSpringer London. The Journal's web site is located at http://www.springer.com/engineering/production+eng/journal/170
Citation
International Journal of Advanced Manufacturing Technology, 2019 How to Cite?
AbstractDue to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.
Persistent Identifierhttp://hdl.handle.net/10722/269428
ISSN
2017 Impact Factor: 2.601
2015 SCImago Journal Rankings: 0.915

 

DC FieldValueLanguage
dc.contributor.authorZhang, YP-
dc.contributor.authorCheng, Y-
dc.contributor.authorWang, X-
dc.contributor.authorZhong, R-
dc.contributor.authorZhang, YF-
dc.contributor.authorTao, F-
dc.date.accessioned2019-04-24T08:07:30Z-
dc.date.available2019-04-24T08:07:30Z-
dc.date.issued2019-
dc.identifier.citationInternational Journal of Advanced Manufacturing Technology, 2019-
dc.identifier.issn0268-3768-
dc.identifier.urihttp://hdl.handle.net/10722/269428-
dc.description.abstractDue to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.-
dc.languageeng-
dc.publisherSpringer London. The Journal's web site is located at http://www.springer.com/engineering/production+eng/journal/170-
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technology-
dc.subjectCommon factors-
dc.subjectData-driven-
dc.subjectEnergy consumption-
dc.subjectIntegration-
dc.subjectSmart production line (SPL)-
dc.titleData-driven smart production line and its common factors-
dc.typeArticle-
dc.identifier.emailZhong, R: zhongzry@hku.hk-
dc.identifier.authorityZhong, R=rp02116-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s00170-019-03469-9-
dc.identifier.scopuseid_2-s2.0-85064345033-
dc.identifier.hkuros297624-
dc.publisher.placeUnited Kingdom-

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