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Driving Intelligent Manufacturing: An Application Study on Digital Twin in Factory Digitalization

Published: 09 June 2023 Publication History

Abstract

With the rapid development of digital transformation, digital twin has got rising attention from both academia and industry. Based on new generation of information technology, digital twin has built an integration of virtual and real world with the ability of interconnection and intelligent inter-operation, which has narrowed the gap between physical and digital world, becoming an important enabler for intelligent manufacturing. The paper mainly discusses the application of digital twin in manufacturing scenarios, and its role in enterprises' digital transformation through intelligent operation and maintenance, virtual debugging, anomaly diagnosis, risk prediction, decision-making assistance, intelligent production scheduling and system optimization, so as to help improve production efficiency and promote digital economy. The paper aims to provide reference for the industry in planning and building a digital twin world, and help with the world's technological evolution and industrial development.

References

[1]
B. Sniderman, M. Mahto, M.J. Cotteleer, Industry 4.0 and manufacturing ecosystems: Exploring the world of connected enterprises, Deloitte Consulting, 1 (2016) 3-14.
[2]
G. Shao, M. Helu, Framework for a digital twin in manufacturing: Scope and requirements, Manufacturing Letters, 24 (2020) 105-107.
[3]
Q. Qi, F. Tao, Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison, Ieee Access, 6 (2018) 3585-3593.
[4]
M. Grieves, Digital twin: manufacturing excellence through virtual factory replication, White paper, 1 (2014) 1-7.
[5]
C. Cimino, E. Negri, L. Fumagalli, Review of digital twin applications in manufacturing, Computers in Industry, 113 (2019) 103130.
[6]
Y. Zheng, S. Yang, H. Cheng, An application framework of digital twin and its case study, Journal of Ambient Intelligence and Humanized Computing, 10 (2019) 1141-1153.
[7]
E. Glaessgen, D. Stargel, The digital twin paradigm for future NASA and US Air Force vehicles, 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, 2012, pp. 1818.
[8]
F. Tao, W. Liu, M. Zhang, T.-l. Hu, Q. Qi, H. Zhang, F. Sui, T. Wang, H. Xu, Z. Huang, Five-dimension digital twin model and its ten applications, Computer integrated manufacturing systems, 25 (2019) 1-18.
[9]
F. Tao, B. Xiao, Q. Qi, J. Cheng, P. Ji, Digital twin modeling, J Manuf Syst, 64 (2022) 372-389.
[10]
F. Tao, M. Zhang, A.Y.C. Nee, Digital twin driven smart manufacturing, Academic Press2019.
[11]
F. Tao, Q. Qi, L. Wang, A. Nee, Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison, Engineering, 5 (2019) 653-661.
[12]
F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, F. Sui, Digital twin-driven product design, manufacturing and service with big data, The International Journal of Advanced Manufacturing Technology, 94 (2018) 3563-3576.
[13]
J. Guo, N. Zhao, L. Sun, S. Zhang, Modular based flexible digital twin for factory design, Journal of Ambient Intelligence and Humanized Computing, 10 (2019) 1189-1200.
[14]
M.G. Kapteyn, K.E. Willcox, Predictive digital twins: Where dynamic data-driven learning meets physics-based modeling, Dynamic Data Driven Applications Systems: Third International Conference, DDDAS 2020, Boston, MA, USA, October 2-4, 2020, Proceedings 3, Springer, 2020, pp. 3-7.
[15]
Y. Xie, K. Lian, Q. Liu, C. Zhang, H. Liu, Digital twin for cutting tool: Modeling, application and service strategy, J Manuf Syst, 58 (2021) 305-312.
[16]
Q. Qi, F. Tao, T. Hu, N. Anwer, A. Liu, Y. Wei, L. Wang, A. Nee, Enabling technologies and tools for digital twin, J Manuf Syst, 58 (2021) 3-21.
[17]
Y. Cao, H. Xiong, C. Zhuang, J. Liu, W. Ning, Dynamic scheduling of complex product discrete assembly workshop based on digital twin, Computer Integrated Manufacturing Systems, 27 (2021) 557-568.
[18]
B. Yao, Z. Zhou, L. Wang, W. Xu, J. Yan, Q. Liu, A function block based cyber-physical production system for physical human–robot interaction, J Manuf Syst, 48 (2018) 12-23.
[19]
P. Zhi-Qiang, Y. Jian-Qiang, L. Zhen, Q. Teng-Hai, S. Jin-Lin, L. Fei-Mo, Knowledge-based and data-driven integrating methodologies for collective intelligence decision making: A survey, Acta Automatica Sinica, 48 (2022) 627-643.
[20]
F. Tao, L. Zhang, Y. Liu, Y. Cheng, L. Wang, X. Xu, Manufacturing service management in cloud manufacturing: overview and future research directions, Journal of Manufacturing Science and Engineering, 137 (2015).
[21]
Y. Cheng, Y. Zhang, P. Ji, W. Xu, Z. Zhou, F. Tao, Cyber-physical integration for moving digital factories forward towards smart manufacturing: a survey, The International Journal of Advanced Manufacturing Technology, 97 (2018) 1209-1221.
[22]
D. Gelernter, Mirror worlds: Or the day software puts the universe in a shoebox... How it will happen and what it will mean, Oxford University Press1993.
[23]
D. Wu, S. Liu, L. Zhang, J. Terpenny, R.X. Gao, T. Kurfess, J.A. Guzzo, A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing, J Manuf Syst, 43 (2017) 25-34.
[24]
R. Senington, F. Baumeister, A. Ng, J. Oscarsson, A linked data approach for the connection of manufacturing processes with production simulation models, Procedia CIRP, 70 (2018) 440-445.
[25]
J. Wang, Y. Ma, L. Zhang, R.X. Gao, D. Wu, Deep learning for smart manufacturing: Methods and applications, J Manuf Syst, 48 (2018) 144-156.
[26]
L. Rivest, A. Bouras, B. Louhichi, Product Lifecycle Management. Towards Knowledge-Rich Enterprises, Springer, 2012.
[27]
Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks, IEEE Transactions on Industrial Informatics, 17 (2020) 5098-5107.
[28]
P. Helo, M. Suorsa, Y. Hao, P. Anussornnitisarn, Toward a cloud-based manufacturing execution system for distributed manufacturing, Computers in Industry, 65 (2014) 646-656.

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    ICIEAEU '23: Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications
    January 2023
    339 pages
    ISBN:9781450398527
    DOI:10.1145/3587889
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 09 June 2023

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    Author Tags

    1. digital twin
    2. industrial internet
    3. intelligent manufacturing
    4. interconnection
    5. production scheduling
    6. visualization

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