A Unified Digital Twin Framework for Real-time Monitoring and Evaluation of Smart Manufacturing Systems

Digital Twin (DT) is one of the key enabling technologies for realizing the promise of Smart Manufacturing (SM) and Industry 4.0 to improve production systems operation. Driven by the generation and analysis of high volume data coming from interconnected cyber and physical spaces, DTs are real-time digital images of physical systems, processes or products that help evaluate and improve business performance. This paper proposes a novel DT architecture for the real-time monitoring and evaluation of large-scale SM systems. An application to a manufacturing flow-shop is presented to illustrate the usefulness of the proposed methodology.

[1]  Andrew Y. C. Nee,et al.  Digital twin driven prognostics and health management for complex equipment , 2018 .

[2]  Dawn M. Tilbury,et al.  A software-defined framework for the integrated management of smart manufacturing systems , 2018 .

[3]  I. Graessler,et al.  Integration of a digital twin as human representation in a scheduling procedure of a cyber-physical production system , 2017, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[4]  Qiang Liu,et al.  Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop , 2018, Journal of Ambient Intelligence and Humanized Computing.

[5]  Andrew Y. C. Nee,et al.  Digital twin-driven product design framework , 2019, Int. J. Prod. Res..

[6]  D.M. Tilbury,et al.  An Approach for Factory-Wide Control Utilizing Virtual Metrology , 2007, IEEE Transactions on Semiconductor Manufacturing.

[7]  Abdulmotaleb El Saddik,et al.  C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems , 2017, IEEE Access.

[8]  Giulia Pedrielli,et al.  Simulation-Predictive Control for Manufacturing Systems , 2018, 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE).

[9]  Eric J. Tuegel,et al.  The Airframe Digital Twin: Some Challenges to Realization , 2012 .

[10]  Sandro Wartzack,et al.  Shaping the digital twin for design and production engineering , 2017 .

[11]  Ernesto López-Mellado,et al.  Input–output identification of controlled discrete manufacturing systems , 2014, Int. J. Syst. Sci..

[12]  Fei Tao,et al.  Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.

[13]  Christian Brecher,et al.  Industrial Internet of Things and Cyber Manufacturing Systems , 2017 .

[14]  James Moyne,et al.  Predictive Maintenance in semiconductor manufacturing , 2015, 2015 26th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC).

[15]  James Moyne,et al.  Dynamic Rerouting of Cyber-Physical Production Systems in Response to Disruptions Based on SDC Framework , 2019, 2019 American Control Conference (ACC).

[16]  He Zhang,et al.  Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.

[17]  Dawn M. Tilbury,et al.  A Centralized Framework for System-Level Control and Management of Additive Manufacturing Fleets , 2018, 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE).