[1]张映锋, 张党, 任杉. 智能制造及其关键技术研究现状与趋势综述[J]. 机械科学与技术, 2019, 38(3): 329-338.
Zhang Y F, Zhang D, Ren S. Survey on current research and future trends of smart manufacturing and its key technologies[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(5):329-338.
[2]贺兴, 艾芊, 朱天怡, 等. 数字孪生在电力系统应用中的机遇和挑战[J]. 电网技术, 2020, 44(6): 2009-2019.
He X, Ai Q, Zhu T Y, et al. Opportunities and challenges of the digital twin in power system applications[J]. Power System Technology, 2020, 44(6):2009-2019.
[3]唐文虎, 陈星宇, 钱瞳, 等. 面向智慧能源系统的数字孪生技术及其应用[J]. 中国工程科学, 2020, 22(4): 74-85.
Tang W H, Chen X Y, Qian T, et al. Technologies and applications of digital twin for developing smart energy systems[J]. Strategic Study of CAE, 2020, 22(4):74-85.
[4]张帆, 葛世荣, 李闯. 智慧矿山数字孪生技术研究综述[J]. 煤炭科学技术, 2020, 48(7): 168-176.
Zhang F, Ge S R, Li C. Research summary on digital twin technology for smart mines[J]. Coal Science and Technology, 2020, 48(7):168-176.
[5]陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25(1): 1-18.
Tao F, Liu W R, Zhang M, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1):1-18.
[6]刘青, 刘滨, 王冠, 等. 数字孪生的模型、问题与进展研究[J]. 河北科技大学学报, 2019, 40(1): 68-78.
Liu Q, Liu B, Wang G, et al. Research on digital twin: Model, problem and progress[J]. Journal of Hebei University of Science and Technology, 2019, 40(1):68-78.
[7]Grieves M, Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems[M]. Berlin, Germany: Springer, Cham, 2017.
[8]Grieves M. Digital twin: Manufacturing excellence through virtual factory replication[EB/OL]. https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication, 2015.
[9]赵敏. 基于RAMI 4.0解读新一代智能制造[J]. 中国工程科学, 2018, 20(4): 90-96.
Zhao M. Understanding of a new generation of intelligent manufacturing based on RAMI 4.0[J]. Strategic Study of CAE, 2018, 20(4):90-96.
[10]姚锡凡, 景轩, 张剑铭, 等. 走向新工业革命的智能制造[J]. 计算机集成制造系统, 2020, 26(9): 2299-2320.
Yao X F, Jing X, Zhang J M, et al. Towards smart manufacturing for new industrial revolution[J]. Computer Integrated Manufacturing Systems, 2020, 26(9):2299-2320.
[11]陶飞, 张贺, 戚庆林, 等. 数字孪生模型构建理论及应用[J]. 计算机集成制造系统, 2021, 27(1): 1-15.
Tao F, Zhang H, Qi Q L, et al. Theory of digital twin modeling and its application[J]. Computer Integrated Manufacturing Systems, 2021, 27(1):1-15.
[12]陶飞, 戚庆林, 王力翚, 等. 数字孪生与信息物理系统——比较与联系[J]. Engineering, 2019, 5(4): 132-149.
Tao F, Qi Q L, Wang L H, et al. Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: Correlation and comparison[J]. Engineering, 2019, 5(4):132-149.
[13]周济, 李培根, 周艳红, 等. 走向新一代智能制造[J]. Engineering, 2018, 4(1): 28-47.
Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing[J]. Engineering, 2018, 4(1):28-47.
[14]刘强. 智能制造理论体系架构研究[J]. 中国机械工程, 2020, 31(1): 24-36.
Liu Q. Study on architecture of intelligent manufacturing theory[J]. China Mechanical Engineering, 2020, 31(1):24-36.
[15]陶飞, 戚庆林. 面向服务的智能制造[J]. 机械工程学报, 2018, 54(16): 11-23.
Tao F, Qi Q L. Service-oriented smart manufacturing[J]. Journal of Mechanical Engineering, 2018, 54(16):11-23.
[16]中国电子技术标准化研究院.信息物理系统白皮书[EB/OL]. http://www.cesi.cn/201703/2251.html,2017.
China Electron-ics Standardization Institute. White paper: Cyber-physical system[EB/OL]. http://www.cesi.cn/201703/2251. html, 2017.
[17]邹萍, 张华, 马凯蒂, 等. 面向边缘计算的制造资源感知接入与智能网关技术研究[J]. 计算机集成制造系统, 2020, 26(1): 40-48.
Zou P, Zhang H, Ma K D,et al. Perception and access of manufacturing resources and intelligent gateway technology for edge computing[J]. Computer Integrated Manufacturing Systems, 2020, 26(1):40-48.
[18]刘大同, 郭凯, 王本宽, 等. 数字孪生技术综述与展望[J]. 仪器仪表学报, 2018, 39(11): 1-10.
Liu D T, Guo K, Wang B K, et al. Summary and perspective survey on digital twin technology[J]. Chinese Journal of Scientific Instrument, 2018, 39(11):1-10.
[19]杨帆, 吴涛, 廖瑞金, 等. 数字孪生在电力装备领域中的应用与实现方法[J]. 高电压技术, 2021, 47(5): 1505-1521.
Yang F, Wu T, Liao R J, et al. Application and implementation method of digital twin in electric equipment[J]. High Voltage Engineering, 2021, 47(5):1505-1521.
[20]赵浩然, 刘检华, 熊辉, 等. 面向数字孪生车间的三维可视化实时监控方法[J]. 计算机集成制造系统, 2019, 25(6): 1432-1443.
Zhao H R, Liu J H, Xiong H, et al. 3D visualization real-time monitoring method for digital twin workshop[J]. Computer Integrated Manufacturing Systems, 2019, 25(6):1432-1443.
[21]郑守国, 张勇德, 谢文添, 等. 基于数字孪生的飞机总装生产线建模[J]. 浙江大学学报:工学版, 2021, 55(5): 843-854.
Zheng S G, Zhang Y D, Xie W T, et al. Aircraft final assembly line modeling based on digital twin[J]. Journal of Zhejiang University:Engineering Science, 2021, 55(5):843-854.
[22]郭具涛, 洪海波, 钟珂珂, 等. 基于数字孪生的航天制造车间生产管控方法[J]. 中国机械工程, 2020, 31(7): 808-814.
Guo J T, Hong H B, Zhong K K, et al. Production management and control method of aerospace manufacturing workshops based on digital twin[J]. China Mechanical Engineering, 2020, 31(7):808-814.
[23]王安邦, 孙文彬, 段国林. 基于数字孪生与深度学习技术的制造加工设备智能化方法研究[J]. 工程设计学报, 2019, 26(6): 666-674.
Wang A B, Sun W B, Duan G L. Research on intelligent method of manufacturing and processing equipment based on digital twin and deep learning technology[J]. Chinese Journal of Engineering Design, 2019, 26(6):666-674.
[24]陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18.
Tao F, Liu W R, Liu J H, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1):1-18.
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