Contextual self-organizing of manufacturing process for mass individualization: a cyber-physical-social system approach

ABSTRACT

Under the tendency of mass individualization demands, social manufacturing has been proposed as a new paradigm. This paper proposes a contextual process configuration model for realizing the mass individualized manufacturing. The Social Internet of Things (SIoT) strategy is introduced to boost sociality and narrow down the contextual computing complexity based on situational awareness in a cyber-physical-social connected space. SIoT monitors spatiotemporal situations in a spontaneous social network of relationships and interactions, and induces relevant smart machines depending on prosumers’ individual demands. Four key enabling techniques of SIoT are detailed, and then a demonstrative implementation together with a case study are presented to address the decentralized intelligence in proactive decision making for mass individualization. It is expected that this study can help readers to gain more understanding on the characteristics and configuration logics of contextual self-organizing of manufacturing process for mass individualization.

KEYWORDS:

• Smart workpiece
• mass individualization
• cyber-physical-social system
• contextual awareness
• social internet of things

Acknowledgments

This work was supported by the National Key R&D Program of China under Grant No. 2018AAA0101704 and 2019YFB1706200; the Science and Technology Planning Project of Guangdong Province of China under Grant No. 2019A050503010, 2019B090916002, and No. 2019A1515011815; the National Natural Science Foundation of China under Grant No. 51705091 and 51675108; the Science and Technology Plan Project of Guangzhou under Grant No. 201804020092; the Shenzhen Science and Technology Innovation Committee under Grant No. JCY20170818100156260.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

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Funding

This work was supported by the National Key R&D Program of China under Grant No. 2018AAA0101704 and 2019YFB1706200; the Science and Technology Planning Project of Guangdong Province of China under Grant No. 2019A050503010, 2019B090916002, and No. 2019A1515011815; the National Natural Science Foundation of China under Grant No. 51705091 and 51675108; the Science and Technology Plan Project of Guangzhou under Grant No. 201804020092; the Shenzhen Science and Technology Innovation Committee under Grant No. JCY20170818100156260.