数字孪生及其应用探索
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摘要
数字孪生是一种集成多物理、多尺度、多学科属性,具有实时同步、忠实映射、高保真度特性,能够实现物理世界与信息世界交互与融合的技术手段。随着数字孪生车间概念的提出,数字孪生在智能制造中的应用潜力得到越来越多的关注。分析了数字孪生在企业应用和理论研究上的进展,基于前期提出的数字孪生的五维结构模型,提出数字孪生驱动的6条应用准则,探索了数字孪生驱动的14类应用设想与实施过程中所需突破的关键问题与技术,为未来开展数字孪生的进一步落地应用提供理论和方法论参考。
Digital twin,as a technology of integrating multi-physics,multi-scale and multidisciplinary attributes,characterized by real-time synchronization,faithful mapping and high fidelity,could realize interaction and integration between physical space and virtual world.At present,digital twin had received great attention from academics and enterprises worldwide.With the advent of Digital Twin Shop-floor(DTS),digital twins were gained more and more attention on the potential of digital twin in smart manufacturing.The present situation of digital twin in enterprise application and theoretical research was discussed.Based on five-dimensional structure models of digital twin,six application principles were presented.On this basis,fourteen typical application of digital twin and their concepts and key scientific problems or technologies were explored.Which could provide theoretical and methodological reference for engineering practice of digital twin in the future.
Digital twin,as a technology of integrating multi-physics,multi-scale and multidisciplinary attributes,characterized by real-time synchronization,faithful mapping and high fidelity,could realize interaction and integration between physical space and virtual world.At present,digital twin had received great attention from academics and enterprises worldwide.With the advent of Digital Twin Shop-floor(DTS),digital twins were gained more and more attention on the potential of digital twin in smart manufacturing.The present situation of digital twin in enterprise application and theoretical research was discussed.Based on five-dimensional structure models of digital twin,six application principles were presented.On this basis,fourteen typical application of digital twin and their concepts and key scientific problems or technologies were explored.Which could provide theoretical and methodological reference for engineering practice of digital twin in the future.
引文
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[25]UHLEMANN T H J,SCHOCK C,LEHMANN C,et al.The digital twin:demonstrating the potential of real time data acquisition in production systems[J].Procedia Manufacturing,2017,9:113-120.
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[27]BAZILEVS Y,DENG X,KOROBENKO A,et al.Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor data[J].Journal of Applied Mechanics,2015,82(9).
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[2]TAO Fei,ZHANG Meng,CHENG Jiangfeng,et al.Digital twin workshop:a new paradigm for future workshop[J].Computer Integrated Manufacturing Systems,2017,23(1):1-9(in Chinese).[陶飞,张萌,程江峰,等.数字孪生车间:一种未来车间运行新模式[J].计算机集成制造系统,2017,23(1):1-9.]
[3]TAO Fei,CHENG Jiangfeng,QI Qinglin,et al.Digital twindriven product design,manufacturing and service with big data[J].The International Journal of Advanced Manufacturing Technology,2017.DOI:10.1007/s00170-017-0233-1.
[4]TAO Fei,ZHANG Meng.Digital twin shop-floor:a new shopfloor paradigm towards smart manufacturing[J].IEEE Access,2017,5:20418-20427.
[5]TAO Fei,CHENG Ying,CHENG Jingfeng,et al.Theories and technologies for cyber-physical fusion in digital twin shopfloor[J].Computer Integrated Manufacturing Systems,2017,23(08):1603-1611(in Chinese).[陶飞,程颖,程江峰,等.数字孪生车间信息物理融合理论与技术[J].计算机集成制造系统,2017,23(08):1603-1611.]
[6]FOURGEAU E,GOMEZ E,ADLI H,et al.System engineering workbench for multi-views systems methodology with3DEXPERIENCE Platform.the aircraft radar use case[M]//Complex Systems Design&Management Asia.Springer International Publishing,2016.
[7]GRIEVES M,VICKERS J.Digital twin:mitigating unpredictable,undesirable emergent behavior in complex systems[M]//Transdisciplinary Perspectives on Complex Systems.Berlin,Germany:Springer International Publishing,2017.
[8]TUEGEL E,INGRAFFEA A,EASON T,et al.Reengineering aircraft structural life prediction using a digital twin[J].International Journal of Aerospace Engineering,2011.DOI:10.1155/2011/154798.
[9]ZHUANG Cunbo,LIU Jianhua,XIONG Hui,et al.Connotation,architecture and trends of product digital twin[J].Computer Integrated Manufacturing Systems,2017,23(04):753-768(in Chinese).[庄存波,刘检华,熊辉,等.产品数字孪生体的内涵、体系结构及其发展趋势[J].计算机集成制造系统,2017,23(04):753-768.]
[10]YU Yong,FAN Shengting,PENG Guanwei,et al.Study on application of digital twin model in product configuration management[J].Aeronautical Manufacturing Technology,2017,526(7):41-45(in Chinese).[于勇,范胜廷,彭关伟,等.数字孪生模型在产品构型管理中应用探讨[J].航空制造技术,2017,526(7):41-45.]
[11]ZHANG Hao,LIU Qiang,CHEN Xin,et al.A digital twinbased approach for designing and multi-objective optimization of hollow glass production line[J].IEEE Access,2017,5:26901-26911.
[12]WANG Junqiang,FAN Guoqiang,YAN Feiyi,et al.Research on initiative scheduling mode for a physical internetbased manufacturing system[J].International Journal of Advanced Manufacturing Technology,2016,84(1):47-58.
[13]ZHANG Yingfeng,QIAN Cheng,LYU Jingxiang,et al.Agent and cyber-physical system based self-organizing and selfadaptive intelligent shop floor[J].IEEE Transactions on Industrial Informatics,2017,13(2):737-747.
[14]QU Ting,PAN Yanghua,LYU Xuan,et al.IoT-based realtime production logistics synchronization mechanism and method toward customer order dynamics[J].Transactions of the Institute of Measurement and Control,2017,39(4):429-445.
[15]QU Ting,ZHANG Kai,LUO Hao,et al.Internet-of-things based dynamic synchronization of production and logistics:mechanism,system and case study[J].Journal of Mechanical Engineering,2015,51(20):36-44(in Chinese).[屈挺,张凯,罗浩,等.物联网驱动的“生产—物流”动态联动机制、系统及案例[J].机械工程学报,2015,51(20):36-44.]
[16]APRISO.Digital twin:manufacturing excellence through virtual factory replication[EB/OL].(2014-05-06).http://www.apriso.com.
[17]GABOR T,BELZNER L,KIERMEIER M,et al.A Simulation-Based Architecture for Smart Cyber-Physical Systems[C]//Proceedings of the IEEE International Conference on Autonomic Computing.Washington,D.C.,USA:IEEE,2016:374-379.
[18]RIOS J,HERNANDEZ J C,OLIVA M,et al.Product Avatar as Digital Counterpart of a Physical Individual Product?:Literature Review and Implications in an Aircraft[C]//Proceedings of the 22nd ISPE-Inc International Conference on Concurrent Engineering,2015:657-666.DOI:10.3233/978-1-61449-544-9-657.
[19]SCHROEDER G N,STEINMETZ C,PEREIRA C E,et al.Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange[J].IFAC-Paper OnLine,2016,49(30):12-17.
[20]AITOR M,GORKA V,AITOR A,et al.Virtualisation process of a sheet metal punching machine within the Industry 4.0vision[J].International Journal on Interactive Design and Manufacturing,2017,11(2):365-373.
[21]MAJUMDAR P K,FAISALHAIDER M,REIFSNIDER K.Multi-physics Response of Structural Composites and Framework for Modeling Using Material Geometry[C]//Proceedings of the AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics,and Materials Conference.Reston,Va.,USA:AIAA,2013,76(9).
[22]SCOTT-EMUAKPOR O,GEORGE T,BECK J,et al.Material Property Determination of Vibration Fatigued DMLS and Cold-rolled Nickel Alloys[C]//Proceedings of the International Gas Turbine Institute Turbo Expo.New York,N.Y.,USA:ASME,2014,V07AT28A008.
[23]RICKS T M,LACY T E,PINEDA E J,et al.Computationally efficient solution of the high-fidelity generalized method of cells micromechanics relations[EB/OL].[2107-01-05].http://www.cavs.msstate.edu/publications/docs/2015/09/140441700_Ricks.pdf.
[24]CAI Y,STARLY B,COHEN P,et al.Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing[C]//Proceedings of the 45th SME North American Manufacturing Research Conference.Amsterdam,the Netherland:Elsevier,2017,10:1031-1042.
[25]UHLEMANN T H J,SCHOCK C,LEHMANN C,et al.The digital twin:demonstrating the potential of real time data acquisition in production systems[J].Procedia Manufacturing,2017,9:113-120.
[26]WANG X V,KEMENY Z,VANCZA J,et al.Human-robot collaborative assembly in cyber-physical production:Classification framework and implementation[J].CIRP AnnalsManufacturing Technology,2017,66(1):5-8.
[27]BAZILEVS Y,DENG X,KOROBENKO A,et al.Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor data[J].Journal of Applied Mechanics,2015,82(9).
[28]BIELEFEDT B,HOCHHALTER J,HARTL D.Computationally efficient analysis of SMA sensory particles embedded in complex aerostructures using a substructure approach[C]//Proceedings of ASME 2015Conference on Smart Materials,Adaptive Structures and Intelligent Systems,2015:V001T02A007.
[29]SESHADRI B R,KRISHNAMURTHY T.Structural health management of damaged aircraft structures using digital twin concept[EB/OL].[2107-01-15].https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20170001027.pdf.