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Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing

IEEE Access · 2017 · Vol. 5 · pp. 20418–20427
Fei TaoMeng Zhang

Abstract

With the developments and applications of the new information technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, a smart manufacturing era is coming. At the same time, various national manufacturing development strategies have been put forward, such as Industry 4.0, Industrial Internet, manufacturing based on Cyber-Physical System, and Made in China 2025. However, one of specific challenges to achieve smart manufacturing with these strategies is how to converge the manufacturing physical world and the virtual world, so as to realize a series of smart operations in the manufacturing process, including smart interconnection, smart interaction, smart control and management, etc. In this context, as a basic unit of manufacturing, shop-floor is required to reach the interaction and convergence between physical and virtual spaces, which is not only the imperative demand of smart manufacturing, but also the evolving trend of itself. Accordingly, a novel concept of digital twin shopfloor (DTS) based on digital twin is explored and its four key components are discussed, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor digital twin data. What is more, the operation mechanisms and implementing methods for DTS are studied and key technologies as well as challenges ahead are investigated, respectively.

Digital Transformation in IndustryManufacturing Process and OptimizationFlexible and Reconfigurable Manufacturing SystemsSmart manufacturingComputer scienceManufacturing engineeringContext (archaeology)Cyber-physical systemBig dataManufacturingKey (lock)Digital manufacturingCloud computing

Funding

  • National Natural Science Foundation of China
  • Beijing Nova Program
  • National Key Research and Development Program of China
Citations
1,283
FWCI
81.45
field-weighted impact
References
41
Percentile
100%
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References
Big Data in product lifecycle management
The International Journal of Advanced Manufacturing Technology · 2015 · 476 citations
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