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A data-driven smart management and control framework for a digital twin shop floor with multi-variety multi-batch production

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Abstract

To solve the problems of strong uncertainty, dynamics, and high complexity during the operation process in a discrete manufacturing shop floor that produces a variety of variable-volume products, a data-driven smart management and control framework of a digital twin shop floor (DTS) is proposed. Its implementation process is analyzed. Five key tasks are illustrated in detail: (1) the construction of a shop floor digital twin (DT) model from the multi-dimensional multi-scale perspective; (2) data acquisition and management technology in a DTS; (3) the real-time data-driven synchronous modeling of the shop floor operating status; (4) the model- and data-driven online prediction of the shop floor operation status; and (5) the multi-agent-based operation decision of a DTS. In addition, for products that are complex to produce on the assembly shop floor, a DT-based smart management and control system named the DT-VPPC is developed. The effectiveness of the proposed method is verified by a specific application example on an assembly shop floor.

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References

  1. Zhuang C, Gong J, Liu J (2021) Digital twin-based assembly data management and process traceability for complex products. J Manuf Syst 58:118–131

    Article  Google Scholar 

  2. Tao F, Zhang M (2017) Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5:20418–20427

    Article  Google Scholar 

  3. Castro L, Wamba SF (2007) An inside look at RFID technology. J Technol Manag Innov 2(1):128–141

    Google Scholar 

  4. Zhu X, Mukhopadhyay SK, Kurata K (2012) A review of RFID technology and its managerial applications in different industries. J Eng Technol Manag 29:152–167

    Article  Google Scholar 

  5. Huang GQ, Zhang YF, Jiang PY (2007) RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts. Robot Comput Integr Manuf 23(4):469–477

    Article  Google Scholar 

  6. Zhang YF, Jiang PY, Huang GQ (2008) RFID-based smart Kanbans for just-in-time manufacturing. Int J Mater Prod Technol 33:170–184

    Article  Google Scholar 

  7. Wang J, Luo Z, Wong EC (2010) RFID-enabled tracking in flexible assembly line. Int J Adv Manuf Technol 46(1–4):351–360

    Article  Google Scholar 

  8. Qu T, Yang H, Huang GQ et al (2012) A case of implementing RFID-based real-time shop-floor material management for household electrical appliance manufacturers. J Intell Manuf 23:2343–2356

    Article  Google Scholar 

  9. Zhang Y, Wang W, Wu N et al (2016) IoT-enabled real-time production performance analysis and exception diagnosis model. IEEE Trans Autom Sci Eng 13(3):1318–1332

    Article  Google Scholar 

  10. Hutabarat W, Oyekan J, Turner C et al (2017) Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing. Proceeding of 2016 Winter Simulation Conference. Washington, DC, USA: IEEE, 2774-2785

  11. Taylor JW, Mcsharry PE (2007) Short-term load forecasting methods: an evaluation based on european data. IEEE Trans Power Syst 22(4):2213–2219

    Article  Google Scholar 

  12. De Giorgi MG, Congedo PM, Malvoni M (2014) Photovoltaic power forecasting using statistical methods: impact of weather data. IET Sci Meas Technol 8(3):90–97

    Article  Google Scholar 

  13. Li D, Chang C, Liu C et al (2013) A new approach for manufacturing forecast problems with insufficient data: the case of TFT-LCDs. J Intell Manuf 24:225–233

    Article  Google Scholar 

  14. Chang C, Li D, Huang Y et al (2015) A novel gray forecasting model based on the box plot for small manufacturing data sets. Appl Math Comput 265:400–408

    MathSciNet  Google Scholar 

  15. Chang C, Lin J, Jin P (2017) A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast. Comput Math Organ Theory 23:409–422

    Article  Google Scholar 

  16. Li H, Yang X, Wang F et al (2016) Stochastic state sequence model to predict construction site safety states through real-time location systems. Saf Sci 84:78–87

    Article  Google Scholar 

  17. Wang J, Zhang J, Wang X (2018) A data driven cycle time prediction with feature selection in a semiconductor wafer fabrication system. IEEE Trans Semicond Manuf 31(1):173–182

    Article  MathSciNet  Google Scholar 

  18. Ji W, Wang L (2017) Big data analytics based fault prediction for shop floor scheduling. J Manuf Syst 43:187–194

    Article  Google Scholar 

  19. Nakata K, Orihara R, Mizuoka Y, Takagi K (2017) A comprehensive big-data-based monitoring system for yield enhancement in semiconductor manufacturing. IEEE Trans Semicond Manuf PP(4):1–1

    Google Scholar 

  20. Ghezavati R, Saidi-Mehrabad M (2011) An efficient hybrid self-learning method for stochastic cellular manufacturing problem: a queuing-based analysis. Expert Syst Appl 38(3):1326–1335

    Article  Google Scholar 

  21. Sharda B, Banerjee A (2013) Robust manufacturing system design using multi objective genetic algorithms, Petri nets and Bayesian uncertainty representation. J Manuf Syst 32(2):315–324

    Article  Google Scholar 

  22. Petrovic D, Duenas A (2006) A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy Sets Syst 157(16):2273–2285

    Article  MathSciNet  Google Scholar 

  23. Al-Hinai N, ElMekkawy TY (2011) Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. Int J Prod Econ 132(2):279–291

    Article  Google Scholar 

  24. Hamzadayi A, Yildiz G (2016) Event driven strategy based complete rescheduling approaches for dynamic m identical parallel machines scheduling problem with a common server. Comput Ind Eng 91:66–84

    Article  Google Scholar 

  25. Li Z, Ierapetritou MG (2010) Rolling horizon based planning and scheduling integration with production capacity consideration. Chem Eng Sci 65(22):5887–5900

    Article  Google Scholar 

  26. Ouelhadj D, Petrovic S (2009) A survey of dynamic scheduling in manufacturing systems. J Sched 12(4):417–431

    Article  MathSciNet  Google Scholar 

  27. Kouiss K, Pierreval H, Mebarki N (1997) Using multi-agent architecture in FMS for dynamic scheduling. J Intell Manuf 8(1):41–47

    Article  Google Scholar 

  28. Zhang Y, Cheng Q, Lv J et al (2017) Agent and cyber-physical system based self-organizing and self-adaptive intelligent shop-floor. IEEE Trans Ind Inf 13(2):737–747

    Article  Google Scholar 

  29. Wang D, Yu Y, Yin Y, Cheng TCE (2021) Multi-agent scheduling problems under multitasking. Int J Prod Res 59(12):3633–3663

    Article  Google Scholar 

  30. Shi L, Guo G, Song X (2021) Multi-agent based dynamic scheduling optimization of the sustainable hybrid flow shop in a ubiquitous environment. Int J Prod Res 59(2):576–597

    Article  Google Scholar 

  31. Tao F, Cheng Y, Cheng J, Zhang M, Qi Q (2017) Theory and technologies for cyber-physical fusion in digital twin shop-floor. Comput Integr Manuf Syst 23(8):1603–1611

    Google Scholar 

  32. Zhuang C, Miao T, Liu J, Xiong H (2021) The connotation of digital twin, and the construction and application method of shop-floor digital twin. Robot Comput Integr Manuf 68:102075

    Article  Google Scholar 

  33. Kong T, Hu T, Zhou T, Ye Y (2021) Data construction method for the applications of shop-floor digital twin system. J Manuf Syst 58:323–328

    Article  Google Scholar 

  34. Zhang H, Qi Q, Tao F (2022) A multi-scale modeling method for digital twin shop-floor. J Manuf Syst 62:417–428

    Article  Google Scholar 

  35. Liu J, Liu J, Zhuang C, Liu Z, Miao T (2021) Construction method of shop-floor digital twin based on MBSE. J Manuf Syst 60:93–118

    Article  Google Scholar 

  36. Boschert S, Rosen R (2016) Digital twin—the simulation aspect//Mechatronic Future. Springer-Verlag, Berlin

    Google Scholar 

  37. Liu Q, Zhang H, Leng J, Chen X (2019) Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. Int J Prod Res 57(12):3903–3919

    Article  Google Scholar 

  38. Leng J, Liu Q, Ye S, Jing J, Wang Y, Zhang C, Zhang D, Chen X (2020) Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model. Robot Comput Integr Manuf 63:101895

    Article  Google Scholar 

  39. Yildiz E, Møller C, Bilberg A (2021) Demonstration and evaluation of a digital twin-based virtual factory. Int J Adv Manuf Technol 114(1):185–203

    Article  Google Scholar 

  40. Friederich J, Francis DP, Lazarova-Molnar S, Mohamed N (2022) A framework for data-driven digital twins for smart manufacturing. Comput Ind 136:103586

    Article  Google Scholar 

  41. Guo D, Zhong RY, Lin P, Lyu Z, Rong Y, Huang GQ (2020) Digital twin-enabled Graduation Intelligent Manufacturing System for fixed-position assembly islands. Robot Comput Integr Manuf 63:101917

    Article  Google Scholar 

  42. Park KT, Nam YW, Lee HS, Im SJ, Noh SD, Son JY, Kim H (2019) Design and implementation of a digital twin application for a connected micro smart factory. Int J Comput Integr Manuf 32(6):596–614

    Article  Google Scholar 

  43. Zhuang C, Liu J, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Technol 96:1149–1163

    Article  Google Scholar 

  44. Zhang H, Zhang G, Yan Q (2019) Digital twin-driven cyber-physical production system towards smart shop-floor. J Ambient Intell Humaniz Comput 10(11):4439–4453

    Article  Google Scholar 

  45. Zheng Y, Yang S, Cheng H (2019) An application framework of digital twin and its case study. J Ambient Intell Humaniz Comput 10(3):1141–1153

    Article  Google Scholar 

  46. Zhang M, Tao F, Huang B, Nee AYC (2021) A physical model and data-driven hybrid prediction method towards quality assurance for composite components. CIRP Ann Manuf Technol 00:1–4

    Google Scholar 

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Acknowledgements

The authors would like to express our sincere gratitude to the anonymous reviewers for the invaluable comments that have improved the quality of the paper. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Funding

This research is financially supported in part by the National Key Research and Development Program of China (2020YFB1710300), and in part by the National Natural Science Foundation of China (52005042), and in part by the National Defense Fundamental Research Foundation of China (JCKY2020203B039), and in part by the Beijing Institute of Technology Research Fund Program for Young Scholars.

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Authors and Affiliations

  1. Laboratory of Digital Manufacturing, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Jiapeng Zhang, Jianhua Liu, Cunbo Zhuang & Haoxin Guo

  2. Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314000, China

    Jianhua Liu & Cunbo Zhuang

  3. Shanghai Institute of Spacecraft Equipment, Shanghai, 200240, China

    Hailong Ma

Authors
  1. Jiapeng Zhang
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  2. Jianhua Liu
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  3. Cunbo Zhuang
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  4. Haoxin Guo
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Corresponding author

Correspondence to Cunbo Zhuang.

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Zhang, J., Liu, J., Zhuang, C. et al. A data-driven smart management and control framework for a digital twin shop floor with multi-variety multi-batch production. Int J Adv Manuf Technol 131, 5553–5569 (2024). https://doi.org/10.1007/s00170-023-10815-5

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