Smart manufacturing based on cyber-physical systems and beyond

Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H. and Liu, Y. (2019) Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing, 30(8), pp. 2805-2817. (doi: 10.1007/s10845-017-1384-5)

154580.pdf - Accepted Version



Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS, IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS)-based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms’ linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration.

Item Type:Articles
Additional Information:The project was supported by the National Natural Science Foundation of China under Grant Nos. 51675186 and 51175187, and the Science and Technology Foundation of Guangdong Province under Grant Nos. 2016B090918035 and 2017A030223002.
Glasgow Author(s) Enlighten ID:Liu, Dr Ying
Authors: Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., and Liu, Y.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Journal of Intelligent Manufacturing
ISSN (Online):1572-8145
Published Online:28 December 2017
Copyright Holders:Copyright © 2017 Springer Science+Business Media, LLC, part of Springer Nature
First Published:First published in Journal of Intelligent Manufacturing 30(8): 2805-2817
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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