科技创新构建新发展格局

面向智能制造的工业数字孪生关键技术特性

  • 陈晓红 ,
  • 刘飞香 ,
  • 艾彦迪 ,
  • 许冠英 ,
  • 陈姣龙 ,
  • 徐雪松 ,
  • 梁伟
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  • 1. 湖南工商大学前沿交叉学院, 长沙 410205;
    2. 中南大学商学院, 长沙 410083;
    3. 中国铁建重工集团股份有限公司, 长沙 410100
陈晓红,中国工程院院士,教授,研究方向为决策理论与决策支持系统、两型社会与生态文明、数据智能与智慧社会,电子信箱:C88877803@163.com

收稿日期: 2022-05-20

  修回日期: 2022-06-07

  网络出版日期: 2022-08-05

基金资助

国家重点研发计划项目(2019YFB1705204)

Key characteristics analysis of industrial digital twins for smart manufacturing

  • CHEN Xiaohong ,
  • LIU Feixiang ,
  • AI Yandi ,
  • XU Guanying ,
  • CHEN Jiaolong ,
  • XU Xuesong ,
  • LIANG Wei
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  • 1. School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China;
    2. School of Business, Central South University, Changsha 410083, China;
    3. China Railway Construction Heavy Industry Corporation Limited, Changsha 410100, China

Received date: 2022-05-20

  Revised date: 2022-06-07

  Online published: 2022-08-05

摘要

综述了数字孪生技术在智能制造领域中产品设计、生产制造、安全维护3方面的研究进展。在工业数字孪生技术背景下,分析了当前应用于智能制造领域的关键技术的一些基本特性,其中包括可交互性、可孪生性、可组合性及可管理性,概述了工业数字孪生技术在产品设计研发、运维管理决策、生产管控3个方面的典型应用场景。展望了面向智能制造的工业数字孪生技术未来发展。

本文引用格式

陈晓红 , 刘飞香 , 艾彦迪 , 许冠英 , 陈姣龙 , 徐雪松 , 梁伟 . 面向智能制造的工业数字孪生关键技术特性[J]. 科技导报, 2022 , 40(11) : 45 -54 . DOI: 10.3981/j.issn.1000-7857.2022.11.005

Abstract

Digital twins, as a breakthrough technology development, has gained a huge impetus in industrial smart manufacturing, thus continually changing the manufacturing landscape. For a comprehensive understanding of smart manufacturing in the context of industrial digital twin technology, the latest research progresses of digital twin technology in the field of smart manufacturing are briefly reviewed in this paper, which include product design, product manufacturing, and safety maintenance. Then, some basic characteristics of the key technology are analyzed from the perspective of industrial digital twin technology, such as interactivity, twinning, composability and manageability. Further, typical application scenarios of digital twining technology in manufacturing are introduced from three aspects: product design and development, operation and maintenance management, and production control. Finally, the industrial digital twin technology for smart manufacturing is summarized and prospected.

参考文献

[1] Zhong R, Xu X, Klotz E, et al. Intelligent manufacturing in the context of industry 4.0:A review[J]. Engineering, 2017, 3(5):616-630.
[2] Tao F, Qi Q, Wang L, et al. Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0:Correlation and comparison[J]. Engineering, 2019, 5(4):653-661.
[3] Yan D X, Sha W N, Wang D W, et al. Digital twin-driven variant design of a 3C electronic product assembly line[J]. Scientific Reports, 2022, 12:3846.
[4] Tao F, Sui F Y, Liu A, et al. Digital twin-driven product design framework[J]. International Journal of Production Research, 2019, 57(12):3935-3953.
[5] Zheng P, Hong Lim K Y. Product family design and optimization:A digital twin-enhanced approach[J]. Procedia CIRP, 2020, 93:246-250.
[6] Zhang M, Sui F Y, Liu A, et al. Digital twin driven smart product design framework[M]//Digital Twin Driven Smart Design. Amsterdam:Elsevier, 2020:3-32.
[7] Ma J, Chen H, M Zhang Y, et al. A digital twin-driven production management system for production workshop[J]. The International Journal of Advanced Manufacturing Technology, 2020, 110(5):1385-1397.
[8] Guo J P, Zhao N, Sun L, et al. Modular based flexible digital twin for factory design[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3):1189-1200.
[9] Fang Y, L Peng C, Lou P, et al. Digital-twin-based job shop scheduling toward smart manufacturing[J]. IEEE Transactions on Industrial Informatics, 2019, 15(12):6425-6435.
[10] Zhou G H, Zhang C, Li Z, et al. Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing[J]. International Journal of Production Research, 2020, 58(4):1034-1051.
[11] Kong T X, Hu T L, Zhou T T, et al. Data construction method for the applications of workshop digital twin system[J]. Journal of Manufacturing Systems, 2021, 58:323-328.
[12] 李仁旺,肖人彬.数字孪生驱动的大数据制造服务模式[J].科技导报, 2020, 38(14):116-125.
[13] Zhang F Q, Bai J Y, Yang D Y, et al. Digital twin datadriven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision[J]. Scientific Reports, 2022, 12:1546.
[14] Guo D Q, Zhong R Y, Rong Y M, et al. Synchronization of shop-floor logistics and manufacturing under IIoT and digital twin-enabled graduation intelligent manufacturing system[J]. IEEE Transactions on Cybernetics, 2021(99):34516385.
[15] Zhou X K, Xu X S, Liang W, et al. Intelligent small object detection for digital twin in smart manufacturing with industrial cyber-physical systems[J]. IEEE Transactions on Industrial Informatics, 2022, 18(2):1377-1386.
[16] Lu Q C, Xie X, Parlikad A K, et al. Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance[J]. Automation in Construction, 2020, 118:103277.
[17] Dai Y Y, Zhang K, Maharjan S, et al. Deep reinforcement learning for stochastic computation offloading in digital twin networks[J]. IEEE Transactions on Industrial Informatics, 2021, 17(7):4968-4977.
[18] Leng J W, Yan D X, Liu Q, et al. ManuChain:combining permissioned blockchain with a holistic optimization model as bi-level intelligence for smart manufacturing[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2020, 50(1):182-192.
[19] Guo J Y, Yang Z J, Chen C H, et al. Real-time prediction of remaining useful life and preventive maintenance strategy based on digital twin[J]. Journal of Computing and Information Science in Engineering, 2021, 21(3):1-14.
[20] Luo W C, Hu T L, Ye Y X, et al. A hybrid predictive maintenance approach for CNC machine tool driven by digital twin[J]. Robotics and Computer-Integrated Manufacturing, 2020, 65:101974.
[21] 刘献礼,李雪冰,丁明娜,等.面向智能制造的刀具全生命周期智能管控技术[J].机械工程学报, 2021, 57(10):196-219.
[22] Xia K S, Sacco C, Kirkpatrick M, et al. A digital twin to train deep reinforcement learning agent for smart manufacturing plants:Environment, interfaces and intelligence[J]. Journal of Manufacturing Systems, 2021, 58:210-230.
[23] Saad A, Faddel S, Youssef T, et al. On the implementation of IoT-based digital twin for networked microgrids resiliency against cyber attacks[J]. IEEE Transactions on Smart Grid, 2020, 11(6):5138-5150.
[24] 刘义,刘晓冬,焦曼,等.基于数字孪生的智能车间管控[J].制造业自动化, 2020, 42(7):148-152.
[25] Schroeder G N, Steinmetz C, Rodrigues R N, et al. A methodology for digital twin modeling and deployment for industry 4.0[J]. Proceedings of the IEEE, 2021, 109(4):556-567.
[26] Zhang H, Qi Q L, Tao F. A multi-scale modeling method for digital twin shop-floor[J]. Journal of Manufacturing Systems, 2022, 62:417-428.
[27] Minerva R, Lee G M, Crespi N. Digital twin in the IoT context:a survey on technical features, scenarios, and architectural models[J]. Proceedings of the IEEE, 2020, 108(10):1785-1824.
[28] Tong X, Liu Q, Pi S W, et al. Real-time machining data application and service based on IMT digital twin[J]. Journal of Intelligent Manufacturing, 2020, 31(5):1113-1132.
[29] Corallo A, Del Vecchio V D, Lezzi M, et al. Shop floor digital twin in smart manufacturing:A systematic literature review[J]. Sustainability, 2021, 13(23):12987.
[30] Kreutz D, Ramos F M V, Veríssimo P E, et al. Softwaredefined networking:A comprehensive survey[J]. Proceedings of the IEEE, 2015, 103(1):14-76.
[31] Heidari P, Lemieux Y, Shami A. QoS assurance with light virtualization-A survey[C]//2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). Piscataway:IEEE Press, 2016:558-563.
[32] Bakshi K. Microservices-based software architecture and approaches[C]//2017 IEEE Aerospace Conference. Piscataway:IEEE Press, 2017:1-8.
[33] 李洪阳,魏慕恒,黄洁,等.信息物理系统技术综述[J].自动化学报, 2019, 45(1):37-50.
[34] Yun S, Park J H, Kim W T. Data-centric middleware based digital twin platform for dependable cyber-physical systems[C]//2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). Piscataway:IEEE Press, 2017:922-926.
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