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PUBLICATIONS

Big Data Analytics for Smart Factories of the Future

Published

Author(s)

Robert Gao, Lihui Wang, Moneer Helu, Roberto Teti

Abstract

Continued advancement of sensors has led to an ever-increasing amount of data of various physical nature to be acquired from production lines. As rich information relevant to the machines and processes are embedded within these "big data," how to effectively and efficiently discover patterns in the big data to enhance productivity and economy has become both a challenge and an opportunity. This paper discusses essential elements of and promising solutions enabled by data science that are critical to processing data of high volume, velocity, variety, and low veracity, towards the creation of added-value in smart factories of the future.
Citation
CIRP Annals-ManufacturingTechnology
Volume
69
Issue
2
Pub Type
Journals

Download Paper

https://doi.org/10.1016/j.cirp.2020.05.002
Local Download

Keywords

Digital Manufacturing System, Information, Learning
Process measurement and control, Manufacturing, Interoperability in manufacturing, Internet of Things (IoT), Information technology, Data and informatics, Cybersecurity and Cyber-physical systems

Citation

Gao, R. , Wang, L. , Helu, M. and Teti, R. (2020), Big Data Analytics for Smart Factories of the Future, CIRP Annals-ManufacturingTechnology, [online], https://doi.org/10.1016/j.cirp.2020.05.002, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=929090 (Accessed October 13, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 17, 2020, Updated October 12, 2021