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Engineering >> 2019, Volume 5, Issue 4 doi: 10.1016/j.eng.2019.01.014

Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison

a School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China

b Department of Production Engineering, KTH Royal Institute of Technology, Stockholm SE-10044, Sweden

c Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore


Received:2018-06-28 Revised:2018-10-09 Accepted: 2019-01-22 Available online:2019-05-25

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State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) have greatly stimulated the development of smart manufacturing. An important prerequisite for smart manufacturing is cyber–physical integration, which is increasingly being embraced by manufacturers. As the preferred means of such integration, cyber–physical systems (CPS) and digital twins (DTs) have gained extensive attention from researchers and practitioners in industry. With feedback loops in which physical processes affect cyber parts and vice versa, CPS and DTs can endow manufacturing systems with greater efficiency, resilience, and intelligence. CPS and DTs share the same essential concepts of an intensive cyber–physical connection, real-time interaction, organization integration, and in-depth collaboration. However, CPS and DTs are not identical from many perspectives, including their origin, development, engineering practices, cyber–physical mapping, and core elements. In order to highlight the differences and correlation between them, this paper reviews and analyzes CPS and DTs from multiple perspectives.


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[1]  Negri E, Fumagalli L, Macchi M. A review of the roles of digital twin in CPSbased production systems. Procedia Manuf 2017;11:939–48. link1

[2]  Tao F, Qi Q. New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Trans Syst Man Cybern Syst 2017;49(1):81–91. link1

[3]  Kusiak A. Smart manufacturing. Int J Prod Res 2018;56(1–2):508–17. link1

[4]  Zhong RY, Xu X, Klotz E, Newman ST. Intelligent manufacturing in the context of Industry 4.0: a review. Engineering 2017;3(5):616–30. link1

[5]  Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L. Toward new-generation intelligent manufacturing. Engineering 2018;4(1):11–20. link1

[6]  Thoben K, Wiesner S, Wuest T. ‘‘Industrie 4.0” and smart manufacturing–a review of research issues and application examples. Int J Automot Technol 2017;11(1):4–19. link1

[7]  Tao F, Qi Q, Liu A, Kusiak A. Data-driven smart manufacturing. J Manuf Syst 2018;48(Part C):157–69. link1

[8]  O’Donovan P, Leahy K, Bruton K, O’Sullivan DTJ. An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J Big Data 2015;2(25):1–26. link1

[9]  Hu L, Xie N, Kuang Z, Zhao K. Review of cyber–physical system architecture. In: Proceedings of the IEEE 15th International Symposium on Object/Component/ Service-Oriented Real-Time Distributed Computing Workshops (ISORCW); 2012 April 11; Shenzhen, China. Washington, DC: IEEE; 2012. p. 25–30. link1

[10]  Liu Y, Peng Y, Wang B, Yao S, Liu Z. Review on cyber–physical systems. IEEE/ CAA J Autom Sin 2017;4(1):27–40. link1

[11]  Lee EA. The past, present and future of cyber–physical systems: a focus on models. Sensors (Basel) 2015;15(3):4837–69. link1

[12]  Grieves M. Digital twin: manufacturing excellence through virtual factory replication. White paper. Melbourne: US Florida Institute of Technology; 2014. link1

[13]  Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F. Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 2018;94(9–12):3563–76. link1

[14]  Parrott A, Warshaw L. Industry 4.0 and the digital twin: manufacturing meets its match. Dallas: Deloitte University Press; 2017. link1

[15]  Gill H. NSF perspective and status on cyber–physical systems. In: NSF Workshop on Cyber–physical Systems; 2006 Oct 16–17; Austin, TX, USA. Alexandria: National Science Foundation; 2006. link1

[16]  Wang S, Wan J, Zhang D, Li D, Zhang C. Towards smart factory for Industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 2016;101:158–68. link1

[17]  Liu Y, Xu X. Industry 4.0 and cloud manufacturing: a comparative analysis. J Manuf Sci Eng 2017;139(3):034701. link1

[18]  Monostori L, Kádár B, Bauernhansl T, Kondoh S, Kumara S, Reinhart G, et al. Cyber–physical systems in manufacturing. CIRP Ann 2016;65(2):621–41. link1

[19]  Glaessgen E, Stargel D. The digital twin paradigm for future NASA and US Air Force vehicles. In: Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/ AHS Adaptive Structures Conference 14th AIAA; 2012 Apr 6; Honolulu, HI, USA. Reston: American Institute of Aeronautics and Astronautics; 2012. p. 1818. link1

[20]  Tao F, Sui F, Liu A, Qi Q, Zhang M, Song B, et al. Digital twin-driven product design framework. Int J Prod Res 2018. link1

[21]  Zhang H, Liu Q, Chen X, Zhang D, Leng J. A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 2017;5:26901–11. link1

[22]  Tao F, Zhang M. Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 2017;5:20418–27. link1

[23]  Uhlemann THJ, Lehamnn C, Steinhilper R. The digital twin: realizing the cyber– physical production system for Industry 4.0. Procedia CIRP 2017;61:335–40. link1

[24]  Tao F, Zhang M, Liu Y, Nee AYC. Digital twin driven prognostics and health management for complex equipment. CIRP Ann 2018;67(1):169–72. link1

[25]  Schleich B, Anwer N, Mathieu L, Wartzack S. Shaping the digital twin for design and production engineering. CIRP Ann 2017;66(1):141–4. link1

[26]  La HJ, Kim SD. A service-based approach to designing cyber physical systems. In: Proceedings of the 9th IEEE/ACIS International Conference on Computer and Information Science (ICIS); 2010 Aug 18–20; Yamagata, Japan. Washington, DC: IEEE; 2010. p. 895–900. link1

[27]  Söderberg R, Wärmefjord K, Carlson JS, Lindkvist L. Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann 2017;66 (1):137–40. link1

[28]  Lee EA. Cyber–physical systems—are computing foundations adequate? In: Position Paper for NSF Workshop on Cyber–Physical Systems: Research Motivation, Techniques and Roadmap. 2006 Oct 16–17; Austin, TX, USA; 2006. link1

[29]  Wang L, Wong B, Shen W, Lang S. A Java 3D-enabled cyber workspace. Commun ACM 2002;45(11):45–9. link1

[30]  Wang L, Orban P, Cunningham A, Lang S. Remote real-time CNC machining for web-based manufacturing. Robot Comput-Integr Manuf 2004;20(6):563–71. link1

[31]  Wang L. Wise-ShopFloor: an integrated approach for web-based collaborative manufacturing. IEEE Trans Syst Man Cybern App Rev 2008;38(4):562–73. link1

[32]  Wang L, Törngren M, Onori M. Current status and advancement of cyber– physical systems in manufacturing. J Manuf Syst 2015;37(Part 2):517–27. link1

[33]  Wang FY. The emergence of intelligent enterprises: from CPS to CPSS. IEEE Intell Syst 2010;25(4):85–8. link1

[34]  He J. Cyber–physical systems. Commun CCF 2010;6(1):25–9. Chinese. link1

[35]  Rajkumar RR, Lee I, Sha L, Stankovic J. Cyber–physical systems: the next computing revolution. In: Proceedings of the 47th Design Automation Conference; 2010 Jul 13–18; Anaheim, CA, USA. New York: ACM; 2010. p. 731–6. link1

[36]  Zhu Q, Rieger C, Basar T. A hierarchical security architecture for cyber–physical systems. In: Proceedings of the 4th IEEE International Symposium on Resilient Control Systems (ISRCS); 2011 Aug 9–11; Boise, ID, USA. Washington, DC: IEEE; 2011. p. 15–20. link1

[37]  Dillon TS, Zhuge H, Wu C, Singh J, Chang E. Web-of-things framework for cyber–physical systems. Concurr Comp Pract E 2011;23(9): 905–23. link1

[38]  DebRoy T, Zhang W, Turner J, Babu SS. Building digital twins of 3D printing machines. Scr Mater 2017;135:119–24. link1

[39]  Vachálek J, Bartalsky´ L, Rovny´ O, Šišmišová D, Morhácˇ M, Lokšík M. The digital twin of an industrial production line within the Industry 4.0 concept. In: Proceedings of the 2017 21st International Conference on Process Control (PC); 2017 Jun 6–9; Štrbské Pleso, Slovakia. Washington, DC: IEEE; 2017. p. 258–62. link1

[40]  Rosen R, von Wichert G, Lo G, Bettenhausen KD. About the importance of autonomy and digital twins for the future of manufacturing. IFACPapersOnLine 2015;48(3):567–72. link1

[41]  Fawzi H, Tabuada P, Diggavi S. Secure estimation and control for cyber– physical systems under adversarial attacks. IEEE Trans Automat Contr 2014;59 (6):1454–67. link1

[42]  Rieger CG, Gertman DI, McQueen MA. Resilient control systems: next generation design research. In: Proceedings of the 2nd IEEE Conference on Human System Interactions (HSI’09); 2009 May 21–23; Catania, Italy. New York: ACM; 2009. p. 632–6. link1

[43]  Zheng P, Wang H, Sang Z, Zhong RY, Liu Y, Liu C, et al. Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front Mech Eng 2018;13(2):137–50. link1

[44]  Guo N, Jia C. Interpretation of ‘‘cyber–physical systems whitepaper”. Inf Tech Standardization 2017;4:36–47. Chinese. link1

[45]  Qi Q, Zhao D, Liao TW, Tao F. Modeling of cyber–physical systems and digital twin based on edge computing, fog computing and cloud computing towards smart manufacturing. In: Proceedings of ASME 2018 13th International Manufacturing Science and Engineering Conference; 2018 Jun 18–22; College Station, TX, USA. New York: ASME; 2018. link1

[46]  Jazdi N. Cyber physical systems in the context of Industry 4.0. In: Proceedings of the 2014 IEEE International Conference on Automation, Quality and Testing, Robotics; 2014 May 22–24; Cluj-Napoca, Romania. Washington, DC: IEEE; 2014. p. 1–4. link1

[47]  Lee J, Bagheri B, Kao HA. A cyber–physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 2015;3:18–23. link1

[48]  Qi Q, Tao F, Zuo Y, Zhao D. Digital twin service towards smart manufacturing. In: Proceedings of the 51st CIRP Conference on Manufacturing Systems (CMS); 2018 May 15–18; Stockholm, Sweden; 2018. link1

[49]  Tao F, Cheng J, Cheng Y, Gu S, Zheng T, Yang H. SDMSim: a manufacturing service supply–demand matching simulator under cloud environment. Robot Comput-Integr Manuf 2017;45:34–46. link1

[50]  Wang L, Haghighi A. Combined strength of holons, agents and function blocks in cyber–physical systems. J Manuf Syst 2016;40(Part 2):25–34. link1

[51]  Hochhalter JD, Leser WP, Newman JA, Glaessgen EH, Gupta VK, Yamakov V, et al. Coupling damage-sensing particles to the digital twin concept. Hanover: NASA Center for AeroSpace Information; 2014. link1

[52]  Schroeder GN, Steinmetz C, Pereira CE, Espindola DB. Digital twin data modeling with AutomationML and a communication methodology for data exchange. IFAC-PapersOnLine 2016;49(30):12–7. link1

[53]  Tao F, Cheng Y, Cheng J, Zhang M, Xu W, Qi Q. Theories and technologies for cyber–physical fusion in digital twin shop-floor. Comput Integr Manuf Syst 2017;23(8):1603–11. Chinese. link1

[54]  Qi Q, Tao F. Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison. IEEE Access 2018;6:3585–93. link1

[55]  Cheng Y, Qi Q, Tao F. New IT-driven manufacturing service management: research status and prospect. China Mech Eng 2018;29(18):2177–88. Chinese. link1

[56]  Wang L, Gao R, Ragai I. An integrated cyber–physical system for cloud manufacturing. In: Proceedings of the ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference; 2014 Jun 9–13; Detroit, MI, USA. New York: ASME; 2014. link1

[57]  Wan J, Yan H, Suo H, Li F. Advances in cyber–physical systems research. KSII Trans Internet Inf Syst 2011;5(11):1891–908. link1

[58]  Shu Z, Wan J, Zhang D, Li D. Cloud-integrated cyber–physical systems for complex industrial applications. Mob Netw Appl 2016;21(5):865–78. link1

[59]  Wang C, Huang K, Li N. Research on CPS system architecture based on artificial intelligence. Sci Mosaic 2012;7:61–4. Chinese. link1

[60]  Canedo A. Industrial IoT lifecycle via digital twins. In: Proceedings of the 11th IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis; 2016 Oct 2–7; Pittsburgh, PA, USA. Washington, DC: IEEE; 2016. p. 29. link1

[61]  Leng J, Zhang H, Yan D, Liu Q, Chen X, Zhang D. Digital twin-driven manufacturing cyber–physical system for parallel controlling of smart workshop. J Amb Intel Hum Comp 2019;10(3):1155–66. link1

[62]  Tao F, Liu W, Liu J, Liu X, Liu Q, Qu T, et al. Digital twin and its potential application exploration. Comput Integr Manuf Syst 2018;24(1):1–18. link1

[63]  Tao F, Liu W, Zhang M, Hu T, Qi Q, et al. Five-dimension digital twin model and its ten applications. Comput Integr Manuf Syst 2019;25(1):1–18. link1

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