Analysis of Visual Knowledge Mapping in Intelligent Manufacturing Via Digital Twin Technology
-
摘要: 近年来智能制造已成为制造业未来发展的重要方向,作为践行智能制造理念与目标的数字孪生技术,为解决智能制造过程中的信息物理融合难题提供了有效手段,受到世界各国的广泛关注。为全面解析数字孪生技术在智能制造领域的研究动态和发展趋势,检索Web of Science(WOS)和中国知网(CNKI)数据库的数据源,采用Citespace5.7软件梳理了2011~2021年发表的国内外相关文献,绘制出科学知识图谱,并从文献特征、科研合作、研究热点及前沿分析等不同维度进行定性与定量分析。最后从技术层面和应用层面两个角度预测数字孪生技术在智能制造领域的发展趋势。Abstract: In recent years, intelligent manufacturing has become an important direction for the development of manufacturing industry. Digital Twin, as an enabling technology to practice the concept and targets of intelligent manufacturing, provides the effective means to solve the cyber-physical integration in the intelligent manufacturing, which has received the extensive attention in the world. In order to comprehensively analyze the research states and development trends of the digital twin technology in the intelligent manufacturing, according to the data sources of Web of Science (WoS) and China National Knowledge Infrastructure (CNKI), CiteSpace5.7 is used to sort out the publications from 2011 to 2021 at home and abroad. The map of scientific knowledge is drawn and analyzed qualitatively and quantitatively from different dimensions, such as literature characteristics, scientific research cooperation, research hotspots and frontiers. Finally, the development tendency of digital twin technology in the intelligent manufacturing is prospected from the angles of technology and application.
-
表 1 数据来源
Table 1. Data sources
名称 英文检索内容 中文检索内容 数据库类型 Web of Science核心合集 CNKI 检索方式 主题检索 主题检索 检索词汇 digital twin、intelligent manufacture、
advanced manufacturing technology、smart manufacturing数字孪生、智能制造、先进制造技术 时间跨度 2011年~2021年 2011年~2021年 文献类型 article、review、letter 期刊、学位论文、会议 检索结果 623篇 296篇 表 2 研究热点高频词统计
Table 2. Keyword prominence statistics
序
号WoS CNKI 高频词 频次 中心度 高频词 频次 中心度 1 Digital twin 314 0.24 数字孪生 248 1.15 2 Industry 4.0 111 0.13 智能制造 163 0.55 3 Cyber physical system 64 0.11 大数据 30 0.03 4 Simulation 48 0.09 人工智能 21 0.04 5 Smart manufacturing 40 0.08 工业互联网 19 0.02 6 Big data 38 0.04 信息物理系统 19 0.08 7 Optimization 37 0.06 制造业 12 0.02 8 Model 31 0.02 数字孪生模型 10 0.03 9 Manufacturing 30 0.02 数字化转型 9 0.01 10 Management 22 0.06 智能工厂 8 0.02 11 Architecture 19 0.03 工业4.0 8 0.03 12 Machine learning 13 0.05 系统建模 6 0.01 13 Augmented reality 13 0.07 全生命周期 6 0.02 14 Prediction 12 0.02 新一代智能制造 5 0.01 15 Artificial intelligence 11 0.03 智能管控 5 0.01 -
[1] DAI H N, WANG H, XU G Q, et al. Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies[J]. Enterprise Information Systems, 2020, 14(9-10): 1279-1303. doi: 10.1080/17517575.2019.1633689 [2] ZHONG R Y, XU C, CHEN C, et al. Big data analytics for physical internet-based intelligent manufacturing shop floors[J]. International Journal of Production Research, 2017, 55(9): 2610-2621. doi: 10.1080/00207543.2015.1086037 [3] LIU Y Y, LI Z H, WANG Z N, et al. Design of the intelligent manufacturing demonstration system based on IoT in the context of industry 4.0[J]. IOP Conference Series: Earth and Environmental Science, 2019, 252(5): 052001. [4] ANTE L. Digital twin technology for smart manufacturing and industry 4.0: a bibliometric analysis of the intellectual structure of the research discourse[J]. Manufacturing Letters, 2021, 27: 96-102. doi: 10.1016/j.mfglet.2021.01.003 [5] LU Q C, PARLIKAD A K, WOODALL P, et al. Developing a dynamic digital twin at building and city levels: a case study of the West Cambridge campus[J]. Journal of Management in Engineering, 2019, 36(3): 05020004. [6] 王普, 张光星, 张姿, 等. 我国航空发动机数字化设计/制造/管理技术现状及其发展[J]. 航空制造技术, 2005(10): 26-31. doi: 10.3969/j.issn.1671-833X.2005.10.021WANG P, ZHANG G X, ZHANG Z, et al. Status and development of digital design/manufacturing/management technology for aero-engine in China[J]. Aeronautical Manufacturing Technology, 2005(10): 26-31. (in Chinese) doi: 10.3969/j.issn.1671-833X.2005.10.021 [7] GRIEVES M W. Product lifecycle management: the new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2(1-2): 71-84. [8] GRIEVES M. Virtually perfect: driving innovative and lean products through product lifecycle management[M]. Cocoa Beach, USA: Space Coast Press, 2011 [9] 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: 154798. [10] 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 [11] 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. doi: 10.7535/hbkd.2019yx01011LIU 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. (in Chinese) doi: 10.7535/hbkd.2019yx01011 [12] 赵敏, 朱铎先. 新工业革命四大术语辨析[J]. 软件和集成电路, 2020(7): 14-21. doi: 10.3969/j.issn.2096-062X.2020.07.004ZHAO M, ZHU D X. Analysis of four terms of new industrial revolution[J]. Software and Integrated Circuit, 2020(7): 14-21. (in Chinese) doi: 10.3969/j.issn.2096-062X.2020.07.004 [13] 郭亮, 张煜. 数字孪生在制造中的应用进展综述[J]. 机械科学与技术, 2020, 39(4): 590-598.GUO L, ZHANG Y. Review on application progress of digital twin in manufacturing[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(4): 590-598. (in Chinese) [14] 李欣, 刘秀, 万欣欣. 数字孪生应用及安全发展综述[J]. 系统仿真学报, 2019, 31(3): 385-392.LI X, LIU X, WAN X X. Overview of digital twins application and safe development[J]. Journal of System Simulation, 2019, 31(3): 385-392. (in Chinese) [15] RAMOS A L, FERREIRA J V, BARCELÓ J. Model-based systems engineering: an emerging approach for modern systems[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2011, 42(1): 101-111. [16] PLANA R. 工业新宣言: 数字孪生与生态系统[J]. 软件和集成电路, 2017(8): 91-92.PLANA R. New industrial declaration: digital twin and ecosystems[J]. Software and Integrated Circuit, 2017(8): 91-92. (in Chinese) [17] Francisco A, Mohammadi N, Taylor J E. Smart city digital twin-enabled energy management: toward real-time urban building energy benchmarking[J]. Journal of Management in Engineering, 2020, 36(2): 04019045.1-04019045.11. [18] ZHOU W, CHEN Q J, MENG S. Knowledge mapping of credit risk research: scientometrics analysis using CiteSpace[J]. Economic Research-Ekonomska Istraživanja, 2019, 32(1): 3457-34784. doi: 10.1080/1331677X.2019.1660202 [19] 管文玉, 凌卫青. 基于文献计量的数字孪生研究可视化知识图谱分析[J]. 计算机集成制造系统, 2020, 26(1): 18-27. doi: 10.13196/j.cims.2020.01.002GUAN W Y, LING W Q. Analysis of visual knowledge mapping of digital twin research based on bibliometrics[J]. Computer Integrated Manufacturing Systems, 2020, 26(1): 18-27. (in Chinese) doi: 10.13196/j.cims.2020.01.002 [20] 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): 653-661. doi: 10.1016/j.eng.2019.01.014 [21] CHENG J F, ZHANG H, TAO F, et al. DT-II: digital twin enhanced industrial internet reference framework towards smart manufacturing[J]. Robotics and Computer-Integrated Manufacturing, 2020, 62: 101881. doi: 10.1016/j.rcim.2019.101881 [22] LIU Q, ZHANG H, LENG J W, et al. Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system[J]. International Journal of Production Research, 2019, 57(12): 3903-3919. doi: 10.1080/00207543.2018.1471243 [23] HELGERS H, SCHMIDT A, LOHMANN L J, et al. Towards autonomous operation by advanced process control-process analytical technology for continuous biologics antibody manufacturing[J]. Processes, 2021, 9(1): 172. doi: 10.3390/pr9010172 [24] ZOBEL-ROOS S, SCHMIDT A, MESTMÄCKER F, et al. Accelerating biologics manufacturing by modeling or: is approval under the QbD and PAT approaches demanded by authorities acceptable without a digital-twin?[J]. Processes, 2019, 7(2): 94. doi: 10.3390/pr7020094 [25] 郭飞燕, 刘检华, 邹方, 等. 数字孪生驱动的装配工艺设计现状及关键实现技术研究[J]. 机械工程学报, 2019, 55(17): 110-132. doi: 10.3901/JME.2019.17.110GUO F Y, LIU J H, ZOU F, et al. Research on the state-of-art, connotation and key implementation technology of assembly process planning with digital twin[J]. Journal of Mechanical Engineering, 2019, 55(17): 110-132. (in Chinese) doi: 10.3901/JME.2019.17.110 [26] 王柏村, 易兵, 刘振宇, 等. HCPS视角下智能制造的发展与研究[J]. 计算机集成制造系统, 2021, 27(10): 2749-2761.WANG B C, YI B, LIU Z Y, et al. Evolution and state-of-the-art of intelligent manufacturing from HCPS perspective[J]. Computer Integrated Manufacturing Systems, 2021, 27(10): 2749-2761. (in Chinese) [27] 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. doi: 10.19650/j.cnki.cjsi.J1804099LIU 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. (in Chinese) doi: 10.19650/j.cnki.cjsi.J1804099 [28] 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. (in Chinese) [29] 林智成. 数字孪生技术框架及其在制造业中的应用[J]. 工业控制计算机, 2020, 33(6): 129-133. doi: 10.3969/j.issn.1001-182X.2020.06.050LIN Z C. Digital twins technology framework and its applications in manufacturing industry[J]. Industrial Control Computer, 2020, 33(6): 129-133. (in Chinese) doi: 10.3969/j.issn.1001-182X.2020.06.050 [30] ALAM K M, EL SADDIK A. C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems[J]. IEEE Access, 2017, 5: 2050-2062. doi: 10.1109/ACCESS.2017.2657006 [31] 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 [32] ZHENG P, LIM K Y H. Product family design and optimization: a digital twin-enhanced approach[J]. Procedia CIRP, 2020, 93: 246-250. doi: 10.1016/j.procir.2020.05.162 [33] Zhang M, Tao F, Huang B, et al. A network-based model robustness improvement method for product quality assurance[J]. CIRP Annals, 2022, 71(1): 381-384. [34] 于勇, 胡德雨, 戴晟, 等. 数字孪生在工艺设计中的应用探讨[J]. 航空制造技术, 2018, 61(18): 26-33.YU Y, HU D Y, DAI S, et al. Study on application of digital twin in process planning[J]. Aeronautical Manufacturing Technology, 2018, 61(18): 26-33. (in Chinese) [35] 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 [36] SÖDERBERG 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 [37] 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 [38] MACCHI M, RODA I, NEGRI E, et al. Exploring the role of digital twin for asset lifecycle management[J]. IFAC-PapersOnLine, 2018, 51(11): 790-795. doi: 10.1016/j.ifacol.2018.08.415 [39] HUANG Y C, LIAO H S. Building prediction model for a machine tool with genetic algorithm optimization on a general regression neural network[J]. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2020, 38(2): 2347-2357. [40] 刘庭煜, 钟杰, 刘洋, 等. 面向车间人员宏观行为数字孪生模型快速构建的小目标智能检测方法[J]. 计算机集成制造系统, 2019, 25(6): 1463-1473.LIU T Y, ZHONG J, LIU Y, et al. Intelligent small object detection approach for fast modeling of digital twin of global human working activities[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1463-1473. (in Chinese) [41] 丁华, 杨亮亮, 杨兆建, 等. 数字孪生与深度学习融合驱动的采煤机健康状态预测[J]. 中国机械工程, 2020, 31(7): 815-823. doi: 10.3969/j.issn.1004-132X.2020.07.007DING H, YANG L L, YANG Z J, et al. Health prediction of shearers driven by digital twin and deep learning[J]. China Mechanical Engineering, 2020, 31(7): 815-823. (in Chinese) doi: 10.3969/j.issn.1004-132X.2020.07.007 [42] LI C Z, MAHADEVAN S, LING Y, et al. A dynamic Bayesian network approach for digital twin[C]//Proceedings of the 19th AIAA Non-Deterministic Approaches Conference. Grapevine, Texas: AIAA, 2017: 1566 [43] 陶剑, 戴永长, 魏冉. 基于数字线索和数字孪生的生产生命周期研究[J]. 航空制造技术, 2017(21): 26-31.TAO J, DAI Y C, WEI R. Study on production lifecycle based on digital thread and digital twin[J]. Aeronautical Manufacturing Technology, 2017(21): 26-31. (in Chinese) [44] CAI H M, XU L D, XU B Y, et al. IoT-based configurable information service platform for product lifecycle management[J]. IEEE Transactions on Industrial Informatics, 2014, 10(2): 1558-1567. doi: 10.1109/TII.2014.2306391 [45] SCHRANZ C, STROHMEIER F, DAMJANOVIC-BEHRENDT V. A digital twin prototype for product lifecycle data management[C]//Proceedings of the IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA). Antalya, Turkey: IEEE, 2020: 1-6 [46] HÜRKAMP A, GELLRICH S, OSSOWSKI T, et al. Combining simulation and machine learning as digital twin for the manufacturing of overmolded thermoplastic composites[J]. Journal of Manufacturing and Materials Processing, 2020, 4(3): 92. doi: 10.3390/jmmp4030092 [47] 费永辉. 基于数字孪生的柔性作业车间动态调度研究[D]. 杭州: 浙江工业大学, 2019FEI Y H. Research on flexible job shop dynamic scheduling based on digital twin[D]. Hangzhou: Zhejiang University of Technology, 2019. (in Chinese) [48] 刘志峰, 陈伟, 杨聪彬, 等. 基于数字孪生的零件智能制造车间调度云平台[J]. 计算机集成制造系统, 2019, 25(6): 1444-1453.LIU Z F, CHEN W, YANG C B, et al. Intelligent manufacturing workshop dispatching cloud platform based on digital twins[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1444-1453. (in Chinese) [49] 任涛, 于劲松, 唐荻音, 等. 基于数字孪生的机载光电探测系统性能退化建模研究[J]. 航空兵器, 2019, 26(2): 75-80. doi: 10.12132/ISSN.1673-5048.2018.0049REN T, YU J S, TANG D Y, et al. Performance degradation prediction theory and method for airborne electro-optical detection system based on digital twin model[J]. Aero Weaponry, 2019, 26(2): 75-80. (in Chinese) doi: 10.12132/ISSN.1673-5048.2018.0049 [50] 邹晓光, 孙体伟. 浅谈机械制造业内部文档数据的安全使用[J]. 哈尔滨轴承, 2014, 35(2): 95-98.Zou X G, Sun B W. Discussion on safe application of document data in machinery manufacturing line[J]. JOURNAL OF HARBIN BEARING, 2014, 35(2): 95-98. (in Chinese) [51] 李浩, 陶飞, 王昊琪, 等. 基于数字孪生的复杂产品设计制造一体化开发框架与关键技术[J]. 计算机集成制造系统, 2019, 25(6): 1320-1336.LI H, TAO F, WANG H Q, et al. Integration framework and key technologies of complex product design-manufacturing based on digital twin[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1320-1336. (in Chinese) [52] 赵阳, 伏晓露, 廖庆妙, 等. 基于数字孪生的智能脉动管控[J]. 航空制造技术, 2020, 63(1-2): 14-20.ZHAO Y, FU X L, LIAO Q M, et al. Intelligent production management and control for aircraft assembly pulsation line based on digital twin[J]. Aeronautical Manufacturing Technology, 2020, 63(1-2): 14-20. (in Chinese) -