Journal article
Data-driven smart manufacturing
Journal of manufacturing systems, Vol.48(PC), pp.157-169
07/2018
DOI: 10.1016/j.jmsy.2018.01.006
Abstract
•The evolution of manufacturing data was reflected in accordance with four ages.•The lifecycle of manufacturing big data was illustrated as a series of phases.•A framework of data driven smart manufacturing is proposed, and the characteristics are discussed.•Several application scenarios of the proposed framework are outlined.•A case is given out to illustrate the implementation of the proposed framework.
The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today’s manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is discussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined.
Details
- Title: Subtitle
- Data-driven smart manufacturing
- Creators
- Fei Tao - Beihang UniversityQinglin Qi - Beihang UniversityAng Liu - University of South WalesAndrew Kusiak - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of manufacturing systems, Vol.48(PC), pp.157-169
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.jmsy.2018.01.006
- ISSN
- 0278-6125
- eISSN
- 1878-6642
- Grant note
- name: Beijing Nova Program in China, award: Z161100004916063; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China (NSFC), award: 51522501; DOI: 10.13039/501100012166, name: National Key Research and Development Program of China, award: 2016YFB1101700
- Language
- English
- Date published
- 07/2018
- Academic Unit
- Industrial and Systems Engineering; Nursing
- Record Identifier
- 9984186964902771
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