Review on Application Progress of Digital Twin in Manufacturing
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摘要: 随着工业4.0、CPS、智能制造等研究的深入,如何解决制造中物理世界与信息世界之间的交互共融成为进一步推进制造业变革的核心问题。在此背景下,学术界和工业界提出了数字孪生的概念及技术体系,用于解决上述难题。为了全面了解数字孪生研究进展,首先梳理了数字孪生的基本概念,综述了其在航空航天、产品、制造设备及制造车间等阶段的应用进展,重点分析了数字孪生与物联网、大数据、CPS之间的联系与区别,最后指出了数字孪生在制造领域的发展趋势。Abstract: With the development of Industry 4.0, CPS and Intelligent manufacturing, how to solve the interaction and integration between the physical world and the information world in manufacturing has become a key problem that further promote the transformation of manufacturing industry. In this context, the concept and technical system of digital twin to solve the above-mentioned problems was put forward in the research and industry. In order to get a comprehensive understanding of digital twin research progress, the basic concept of digital twin is sorted out, and the application progress of digital twin are summarized from the aerospace, product, manufacturing equipment and manufacturing workshop. The relationship and difference among the digital twin and Internet of things, big data and CPS are analyzed. Finally, the development trend of digital twin in manufacturing are pointed out.
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Key words:
- digital twin /
- internet of things /
- big data /
- cyber-physical system
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图 1 数字孪生的构成[5]
表 1 学术/工业界对数字孪生的定义
机构/作者 年份 定义 美国空军研究实验室和NASA[10] 2011 一种面向飞行器或系统的高集成度多物理场、多尺度、多概率的仿真模型, 能够利用物理模型、传感器数据和历史数据等反映与该模型对应实体的功能、实时状态及演变趋势。 Edward Glaessgen
David Stargel[11]2012 数字孪生是一个综合多物理、多尺度、多概率模拟的复杂系统, 使用最佳的物理模型, 传感器更新, 飞行器历史等, 镜像其相应飞行器数字孪生的生命。 Michael Grieves
John Vickers[5]2017 数字孪生是从微观原子级到宏观几何级全面描述潜在生产或实际制造产品的虚拟信息结构。构建数字孪生的最佳结果是, 任何可以通过检测实际制造产品所获得的信息, 都可以从它的数字孪生中获得。 庄存波等[6] 2017 产品数字孪生体是指物理实体的工作状态和工作进展在信息空间的全要素重建及数字化映射, 是一个集成多物理、多尺度、超写实、动态概率的仿真模型, 可用来模拟、监控、诊断、预测、控制产品物理实体在现实环境中的生产过程、状态和行为。 陶飞等[12] 2018 数字孪生是产品全生命周期(PLM)的一个组成部分, 利用产品生命周期中的物理数据、虚拟数据和交互数据对产品进行实时映射。 Haag Sebastian
Anderl Reiner[13]2018 数字孪生是单个产品的全面数字化表示, 它通过模型和数据包括实际生命对象的属性、条件以及行为, 数字孪生是一组可以模拟它在已部署环境中实际行为的现实模型。 表 2 国内外数字孪生研究情况
应用对象 年份 研究内容 目标 飞行器 2011~
至今1.利用超高保真模型, 对飞行过程中的局部损伤
和组织变化进行探测;
2.结合数字孪生模型对飞行器进行实时监测;
3.利用数字孪生模型对飞行器健康状况进行
评估。1.减少结构件“意外”失效;
2.飞行器损伤(疲劳裂纹、复合材料蠕变等)
预测;
3.飞行器寿命预测;
4.飞行器状态管理。产品 2015~
至今1.利用数字孪生模型进行产品的个性化生产;
2.将产品数字孪生模型融入到产品设计与生产
过程。1.实现产品快速设计, 提高生产效率;
2.实现个性化产品定制;
3.实现模块化设计和高度可伸缩性生产。制造设备 2016~
至今1.对3D打印机建立数字孪生模型;
2.对数控机床建立数字孪生模型;
3.对自动导引运输车(AGV)建立数字孪生模型。1.减少实验次数和生产缺陷;
2.对机器故障进行诊断和预测;
3.实现自动控制、参数可视化以及实时状态监控;
4.准确定位与自动路径规划。制造过程/制造车间 2016~
至今1.探索产品数字孪生模型在制造过程中的应用;
2.探索制造车间虚拟化的机制与实现方法;
3.探索数字孪生在中小制造企业中的应用。1.自动规划产品生产、装配过程, 优化生产资源;
2.提高车间制造设备工作效率, 优化生产过程;
3.利用学习工厂实现制造业向智能制造升级。 -
[1] 周济.智能制造——"中国制造2025"的主攻方向[J].中国机械工程, 2015, 26(17):2273-2284 doi: 10.3969/j.issn.1004-132X.2015.17.001Zhou J. Intelligent manufacturing——main direction of "Made in China 2025"[J]. China Mechanical Engineering, 2015, 26(17):2273-2284(in Chinese) doi: 10.3969/j.issn.1004-132X.2015.17.001 [2] 唐堂, 滕琳, 吴杰, 等.全面实现数字化是通向智能制造的必由之路——解读《智能制造之路:数字化工厂》[J].中国机械工程, 2018, 29(3):366-377 doi: 10.3969/j.issn.1004-132X.2018.03.018Tang T, Teng L, Wu J, et al. Full digitization is the only way to intelligent manufacturing-interpretation of "intelligent manufacturing road:digital factory"[J]. China Mechanical Engineering, 2018, 29(3):366-377(in Chinese) doi: 10.3969/j.issn.1004-132X.2018.03.018 [3] Grieves M W. Product lifecycle management:the new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2(1-2):71-84 http://www.researchgate.net/publication/247833967_Product_lifecycle_management_the_new_paradigm_for_enterprises [4] Githens G. Product lifecycle management:driving the next generation of lean thinking by Michael Grieves[J]. The Journal of Product Innovation Management, 2007, 24(3):278-280 doi: 10.1111/j.1540-5885.2007.00250_2.x [5] Grieves M, Vickers J. Digital Twin: mitigating unpredictable, undesirable emergent behavior in complex systems[M]//Kahlen F J, Flumerfelt S, Alves A. Transdisciplinary Perspectives on Complex Systems. Switzerland: Springer, 2017: 85-113 [6] 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(4):753-768 http://d.old.wanfangdata.com.cn/Periodical/jsjjczzxt201704010Zhuang C B, Liu J H, Xiong H, et al. Connotation, architecture and trends of product digital twin[J]. Computer Integrated Manufacturing Systems, 2017, 23(4):753-768(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/jsjjczzxt201704010 [7] Rosen R, von Wichert G, Lo G, et al. About the importance of autonomy and digital twins for the future of manufacturing[J]. IFAC-PapersOnLine, 2015, 48(3):567-572 doi: 10.1016/j.ifacol.2015.06.141 [8] NASA. NASA working on early version of 'Star-Trek'-like main ship computer[EB/OL]. https: //www.nasa.gov/vision/earth/technologies/Virtual_Iron_Bird_jb.html, 2018-10-20 [9] 张玉良, 张佳朋, 王小丹, 等.面向航天器在轨装配的数字孪生技术[J].导航与控制, 2018, 17(3):75-82 doi: 10.3969/j.issn.1674-5558.2018.03.012Zhang Y L, Zhang J P, Wang X D, et al. Digital twin technology for spacecraft on-orbit assembly[J]. Navigation and Control, 2018, 17(3):75-82(in Chinese) doi: 10.3969/j.issn.1674-5558.2018.03.012 [10] Tugel E J, Ingraffea A R, Eason T G, et al. Reengineering aircraft structural life prediction using a digital twin[J]. International Journal of Aerospace Engineering, 2011, 2011:Article ID 154798 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_9cf3cdeab473a511233ebade58029fb6 [11] Glaessgen E, Stargel D. The digital twin paradigm for future NASA and U.S. air force vehicles[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Honolulu, Hawaii: AIAA, 2012 [12] Tao F, Cheng J F, Qi Q L, et al. Digital twin-driven product design, manufacturing and service with big data[J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(9-12):3563-3576 doi: 10.1007/s00170-017-0233-1 [13] Haag S, Anderl R. Digital twin-proof of concept[J]. Manufacturing Letters, 2018, 15:64-66 doi: 10.1016/j.mfglet.2018.02.006 [14] Shafto M, Conroy M, Doyle R, et al. Modeling, simulation, information technology & processing roadmap[EB/OL]. (2018-10-20). https://www.nasa.gov/pdf/501321main_TA11-ID_rev4_NRC-wTASR.pdf [15] 陈振, 丁晓, 唐健钧, 等.基于数字孪生的飞机装配车间生产管控模式探索[J].航空制造技术, 2018, 61(12):46-50 http://d.old.wanfangdata.com.cn/Periodical/hkgyjs201812006Chen Z, Ding X, Tang J J, et al. Digital twin-based production management and control mode for aircraft assembly shop-floor[J]. Aeronautical Manufacturing Technology, 2018, 61(12):46-50(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/hkgyjs201812006 [16] Tao F, Zhang H, Liu A, et al. Digital twin in industry:state-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2019, 15(4):2405-2415 doi: 10.1109/TII.2018.2873186 [17] Cerrone A, Hochhalter J, Heber G, et al. On the Effects of modeling as-manufactured geometry:toward digital twin[J]. International Journal of Aerospace Engineering, 2014, 2014:Article ID 439278 https://www.hindawi.com/journals/ijae/2014/439278/ [18] Tuegel E J, Ingraffea A R, Eason T G, et al. Reengineering aircraft structural life prediction using a digital twin[J]. International Journal of Aerospace Engineering, 2011, 2011:Article ID 154798 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_9cf3cdeab473a511233ebade58029fb6 [19] Li C Z, Mahadevan S, Ling Y, et al. Dynamic Bayesian network for aircraft wing health monitoring digital twin[J]. AIAA Journal, 2017, 55(3):930-941 doi: 10.2514/1.J055201 [20] 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 doi: 10.1080/00207543.2018.1443229 [21] S derberg R, Wärmefjord K, Carlson J S, et al. Toward a digital twin for real-time geometry assurance in individualized production[J]. CIRP Annals, 2017, 66(1):137-140 doi: 10.1016/j.cirp.2017.04.038 [22] Um J, Weyer S, Quint F. Plug-and-simulate within modular assembly line enabled by digital twins and the use of automationML[J]. IFAC-PapersOnLine, 2017, 50(1):15904-15909 doi: 10.1016/j.ifacol.2017.08.2360 [23] Schleich B, Anwer N, Mathieu L, et al. Shaping the digital twin for design and production engineering[J]. CIRP Annals, 2017, 66(1):141-144 doi: 10.1016/j.cirp.2017.04.040 [24] 戴晟, 赵罡, 于勇, 等.数字化产品定义发展趋势:从样机到孪生[J].计算机辅助设计与图形学学报, 2018, 30(8):1554-1562 http://d.old.wanfangdata.com.cn/Periodical/jsjfzsjytxxxb201808018Dai S, Zhao G, Yu Y, et al. Trend of digital product definition:from mock-up to twin[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(8):1554-1562(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/jsjfzsjytxxxb201808018 [25] Debroy T, Zhang W, Turner J, et al. Building digital twins of 3D printing machines[J]. Scripta Materialia, 2017, 135:119-124 doi: 10.1016/j.scriptamat.2016.12.005 [26] Cai Y, Starly B, Cohen P, et al. Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing[J]. Procedia Manufacturing, 2017, 10:1031-1042 doi: 10.1016/j.promfg.2017.07.094 [27] Scaglioni B, Ferretti G. Towards digital twins through object-oriented modelling:a machine tool case study[J]. IFAC-PapersOnLine, 2018, 51(2):613-618 doi: 10.1016/j.ifacol.2018.03.104 [28] Botkina D, Hedlind M, Olsson B, et al. Digital Twin of a cutting tool[J]. Procedia CIRP, 2018, 72:215-218 doi: 10.1016/j.procir.2018.03.178 [29] Luo W C, Hu T L, Zhang C R, et al. Digital twin for CNC machine tool:modeling and using strategy[J]. Journal of Ambient Intelligence & Humanized Computing, 2019, 10(3):1129-1140 doi: 10.1007/s12652-018-0946-5 [30] Sierla S, Kyrki V, Aarnio P, et al. Automatic assembly planning based on digital product descriptions[J]. Computers in Industry, 2018, 97:34-46 doi: 10.1016/j.compind.2018.01.013 [31] Tao F, Zhang M. Digital twin shop-floor:a new shop-floor paradigm towards smart manufacturing[J]. IEEE Access, 2017, 5:20418-20427 doi: 10.1109/ACCESS.2017.2756069 [32] 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 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjjczzxt201701001Tao F, Zhang M, Cheng J F, et al. Digital twin workshop:a new paradigm for future workshop[J]. Computer Integrated Manufacturing Systems, 2017, 23(1):1-9(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjjczzxt201701001 [33] Zhang H, Liu Q, Chen X, et al. A digital twin-based approach for designing and multi-objective optimization of hollow glass production line[J]. IEEE Access, 2017, 5:26901-26911 doi: 10.1109/ACCESS.2017.2766453 [34] Uhlemann T H J, Lehmann C, Steinhilper R. The digital twin:realizing the cyber-physical production system for industry 4.0[J]. Procedia CIRP, 2017, 61:335-340 doi: 10.1016/j.procir.2016.11.152 [35] Graessler I, Poehler A. Intelligent control of an assembly station by integration of a digital twin for employees into the decentralized control system[J]. Procedia Manufacturing, 2018, 24:185-189 doi: 10.1016/j.promfg.2018.06.041 [36] Rosen R, Von Wichert G, Lo G, et al. About the importance of autonomy and digital twins for the future of manufacturing[J]. IFAC-PapersOnLine, 2015, 48(3):567-572 doi: 10.1016/j.ifacol.2015.06.141 [37] 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 doi: 10.1016/j.promfg.2017.04.043 [38] van Kranenburg R. The internet of things: a critique of ambient technology and the All-seeing network of RFID[EB/OL]. (2018-10-20). http://ss.zhizhen.com/detail.2018 [39] Xu L D, He W, Li S C. Internet of things in industries:a survey[J]. IEEE Transactions on Industrial Informatics, 2014, 10(4):2233-2243 doi: 10.1109/TII.2014.2300753 [40] He Y, Guo J C, Zheng X L. From surveillance to digital twin:challenges and recent advances of signal processing for industrial internet of things[J]. IEEE Signal Processing Magazine, 2018, 35(5):120-129 doi: 10.1109/MSP.2018.2842228 [41] Gupta A, Tsai T, Rueb D, et al. Forecast: internet of things-endpoints and associated services, worldwide, 2017[Z]. 2017 [42] Sun C L. Application of RFID technology for logistics on internet of things[J]. AASRI Procedia, 2012, 1:106-111 doi: 10.1016/j.aasri.2012.06.019 [43] Fan J Q, Han F, Liu H. Challenges of big data analysis[J]. National Science Review, 2014, 1(2):293-314 doi: 10.1093/nsr/nwt032 [44] Jagadish H V, Gehrke J, Labrinidis A, et al. Big data and Its technical challenges[J]. Communications of the ACM, 2014, 57(7):86-94 doi: 10.1145/2611567 [45] Qi Q L, Tao F. Digital twin and big data towards smart manufacturing and industry 4.0:360 degree comparison[J]. IEEE Access, 2018, 6:3585-3593 doi: 10.1109/ACCESS.2018.2793265 [46] Issa N T, Byers S W, Dakshanamurthy S. Big data:the next frontier for innovation in therapeutics and healthcare[J]. Expert Review of Clinical Pharmacology, 2014, 7(3):293-298 doi: 10.1586/17512433.2014.905201 [47] Wang L H, Törngren M, Onori M. Current status and advancement of cyber-physical systems in manufacturing[J]. Journal of Manufacturing Systems, 2015, 37:517-527 doi: 10.1016/j.jmsy.2015.04.008 [48] Sanislav T, Miclea L. Cyber-physical systems-Concept, challenges and research areas[J]. Control Engineering and Applied Informatics, 2012, 14(2):28-33 http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC0214385907/ [49] Lee J, Bagheri B, Kao H A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems[J]. Manufacturing Letters, 2015, 3:18-23 doi: 10.1016/j.mfglet.2014.12.001 [50] Monostori L. Cyber-physical production systems:Roots, expectations and R & D challenges[J]. Procedia CIRP, 2014, 17:9-13 doi: 10.1016/j.procir.2014.03.115