Systematic review of digital twin technology and applications

Junfeng Yao1, Yang Yang1, Xuecheng Wang1, Xiaopeng Zhang2
1Center for Digital Media Computing, School of Film, Xiamen University, Xiamen 361005, China
2State Key Laboratory of Multimodal Artificial Intelligence Systems, the Institute of Automation, Chinese Academy of Sciences, Beijing 101408, China

Tóm tắt

AbstractAs one of the most important applications of digitalization, intelligence, and service, the digital twin (DT) breaks through the constraints of time, space, cost, and security on physical entities, expands and optimizes the relevant functions of physical entities, and enhances their application value. This phenomenon has been widely studied in academia and industry. In this study, the concept and definition of DT, as utilized by scholars and researchers in various fields of industry, are summarized. The internal association between DT and related technologies is explained. The four stages of DT development history are identified. The fundamentals of the technology, evaluation indexes, and model frameworks are reviewed. Subsequently, a conceptual ternary model of DT based on time, space, and logic is proposed. The technology and application status of typical DT systems are described. Finally, the current technical challenges of DT technology are analyzed, and directions for future development are discussed.

Từ khóa


Tài liệu tham khảo

Garner TA, Powell WA, Carr V (2016) Virtual carers for the elderly: A case study review of ethical responsibilities. Digit Health 2:2055207616681173. https://doi.org/10.1177/2055207616681173

Biahmou A, Emmer C, Pfouga A, Stjepandić J (2016) Digital master as an enabler for industry 4.0. In: Proceedings of the 23rd ISPE Inc. International Conference on Transdisciplinary Engineering, IOS Press, Curitiba, 3-7 October 2016. https://doi.org/10.3233/978-1-61499-703-0-672

Schuh G, Rebentisch E, Riesener M, Ipers T, Tönnes C, Jank MH (2019) Data quality program management for digital shadows of products. Proced CIRP 86:43-48. https://doi.org/10.1016/j.procir.2020.01.027

Boschert S, Rosen R (2016) Digital twin-the simulation aspect. In: Hehenberger P, Bradley D (eds) Mechatronic futures, Springer, Cham, pp 59-74. https://doi.org/10.1007/978-3-319-32156-1_5

Lo CK, Chen CH, Zhong RY (2021) A review of digital twin in product design and development. Adv Eng Informat 48:101297. https://doi.org/10.1016/j.aei.2021.101297

Tao F, Zhang H, Qi QL, Zhang M, Liu WR, Cheng JF et al (2020) Ten questions towards digital twin: analysis and thinking. Comp Integrat Manufact Syst 26(1):1-17. https://doi.org/10.13196/j.cims.2020.01.001

Grieves M, Vickers J (2017) Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen FJ, Flumerfelt S, Alves A (eds) Transdisciplinary perspectives on complex systems, Springer, Cham, pp 85-113. https://doi.org/10.1007/978-3-319-38756-7_4

Negri E, Fumagalli L, Macchi M (2017) A review of the roles of digital twin in CPS-based production systems. Procedia Manuf 11:939-948. https://doi.org/10.1016/j.promfg.2017.07.198

Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and U.S. air force vehicles. In: Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA, Honolulu, 23 April 2012. https://doi.org/10.2514/6.2012-1818

Lee J, Lapira E, Bagheri B, Kao HA (2013) Recent advances and trends in predictive manufacturing systems in big data environment. Manuf Lett 1(1):38-41. https://doi.org/10.1016/j.mfglet.2013.09.005

Rosen R, Von Wichert G, Lo G, Bettenhausen KD (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3):567-572. https://doi.org/10.1016/j.ifacol.2015.06.141

Zhuang CB, Liu JH, Xiong H, Ding XY, Liu SL, Weng G (2017) Connotation, architecture and trends of product digital twin. Comput Integr Manuf Syst 23(4):753-768. https://doi.org/10.13196/j.cims.2017.04.010

Tao F, Cheng JF, Qi QL, Zhang M, Zhang H, Sui FY (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94(9):3563-3576. https://doi.org/10.1007/s00170-017-0233-1

Haag S, Anderl R (2018) Digital twin-proof of concept. Manuf Lett 15:64-66. https://doi.org/10.1016/j.mfglet.2018.02.006

Adnan MF, Daud MF, Saud MS (2014) Contextual knowledge in three-dimensional computer aided design (3D CAD) modeling: A literature review and conceptual framework. In: Proceedings of the 2014 international conference on teaching and learning in computing and engineering, IEEE, Kuching, 11-13 April 2014. https://doi.org/10.1109/LaTiCE.2014.41

Nzetchou S, Durupt A, Remy S, Eynard B (2019) Review of CAD visualization standards in PLM. In: Fortin C, Rivest L, Bernard A, Bouras A (eds) Product lifecycle management in the digital twin era. 16th IFIP WG 5.1 international conference, PLM 2019, Moscow, Russia, July 8-12, 2019. IFIP advances in information and communication technology, vol 565. Springer, Cham, pp 34-43

Nie RM, Zhou XY, Xiao J, Zhao B (2022) Analysis and perspective on digital twin technology. Astronaut Syst Eng Technol 6(1):1-6

Tuegel E (2012) The airframe digital twin: some challenges to realization. In: Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA, Honolulu, 23-26 April 2012. https://doi.org/10.2514/6.2012-1812

Ríos J, Hernández JC, Oliva M, Mas F (2015) Product avatar as digital counterpart of a physical individual product: Literature review and implications in an aircraft. In: Proceedings of the 22nd ISPE-Inc International Conference on Concurrent Engineering, Delft Univ Technol, Delft, 20-23 July 2015. https://doi.org/10.3233/978-1-61499-544-9-657

Han DC (2020) Research on strategies of “information-physical” interaction oriented to building digital twin. Dissertation, Tsinghua University

Nzetchou S, Durupt A, Remy S, Eynard B (2022) Semantic enrichment approach for low-level CAD models managed in PLM context: Literature review and research prospect. Comput Ind 135:103575. https://doi.org/10.1016/j.compind.2021.103575

Zeng Y, Kim KY, Raskin V, Fung BCM, Kitamura Y (2013) Modeling, extraction, and transformation of semantics in computer aided engineering systems. Adv Eng Inform 27(1):1-3. https://doi.org/10.1016/j.aei.2012.12.001

Zhang YY (2021) Application of computer software simulation technology in physics experiment teaching. In: Proceedings of the 2021 4th International Conference on Information Systems and Computer Aided Education, Association for Computing Machinery, Dalian, 24-26 September 2021. https://doi.org/10.1145/3482632.3483197

Lv ZH, Ota K, Lloret J, Xiang W, Bellavista P (2022) Complexity problems handled by advanced computer simulation technology in smart cities 2021. Complexity 2022:9847249. https://doi.org/10.1155/2022/9847249

Wu YH (2022) Design and implementation of financial learning system based on computer simulation technology. In: Proceedings of the 2021 3rd international conference on artificial intelligence and advanced manufacture, Association for Computing Machinery, Manchester, 23-25 October 2021. https://doi.org/10.1145/3495018.3495411

Morimoto G, Koyama YM, Zhang H, Komatsu TS, Ohno Y, Nishida K et al (2021) Hardware acceleration of tensor-structured multilevel ewald summation method on MDGRAPE-4A, a special-purpose computer system for molecular dynamics simulations. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Association for Computing Machinery, St. Louis, 12-17 November 2023. https://doi.org/10.1145/3458817.3476190

Cárdenas-Robledo LA, Herández-Uribe Ó, Reta C, Cantoral-Ceballos JA (2022) Extended reality applications in industry 4.0.-a systematic literature review. Telemat Inf 73:101863. https://doi.org/10.1016/j.tele.2022.101863

Al-Sabbag ZA, Yeum CM, Narasimhan S (2022) Interactive defect quantification through extended reality. Adv Eng Inform 51:101473. https://doi.org/10.1016/j.aei.2021.101473

Krasnyanskiy MN, Obukhov AD, Dedov DL (2022) Control system for an adaptive running platform for moving in virtual reality. Automat Remote Control 83(3):355-366. https://doi.org/10.1134/S0005117922030055

Mäkinen H, Haavisto E, Havola S, Koivisto JM (2022) User experiences of virtual reality technologies for healthcare in learning: an integrative review. Behav Inform Technol 41(1):1-17. https://doi.org/10.1080/0144929X.2020.1788162

Shao L, Yang S, Fu TY, Lin YC, Geng HX, Ai DN et al (2022) Augmented reality calibration using feature triangulation iteration-based registration for surgical navigation. Comput Biol Med 148:105826. https://doi.org/10.1016/j.compbiomed.2022.105826

Ghasemi Y, Jeong H, Choi SH, Park KB, Lee JY (2022) Deep learning-based object detection in augmented reality: A systematic review. Comput Ind 139:103661. https://doi.org/10.1016/j.compind.2022.103661

Howard MC, Davis MM (2022) A meta-analysis and systematic literature review of mixed reality rehabilitation programs: Investigating design characteristics of augmented reality and augmented virtuality. Comput Human Behav 130:107197. https://doi.org/10.1016/j.chb.2022.107197

Ning HS, Wang H, Lin YJ, Wang WX, Dhelim S, Farha F et al (2021) A survey on metaverse: the state-of-the-art, technologies, applications, and challenges. arXiv: 2111.09673. https://doi.org/10.48550/ARXIV.2111.09673

Wang GS, Han YJ (2022) Metaverse: current practices, trends and implications for future research. Shanghai Manag Sci 44(5):1-6

Wu J, Cao Z, Chen P, He CY, Ke D (2022) User’s information behavior from the perspective of metaverse: framework and prospect. J Inf Resour Manag 12(1):4-20. https://doi.org/10.13365/j.jirm.2022.01.004

Qian RJ, Li BW, Song HQ, Wu XL, Zhang Y (2022) Application of improved multi-verse optimization algorithm in model correction of aero-engine with unheating. J Propuls Technol 43(5):40-49. https://doi.org/10.13675/j.cnki.tjjs.200222

Ferko E, Bucaioni A, Behnam M (2022) Architecting digital twins. IEEE Access 10:50335-50350. https://doi.org/10.1109/ACCESS.2022.3172964

Lee J, Lapira E, Yang SH, Kao A (2013) Predictive manufacturing system - trends of next-generation production systems. IFAC Proc Vol 46(7):150-156. https://doi.org/10.3182/20130522-3-BR-4036.00107

Grieves MW (2005) Product lifecycle management: the new paradigm for enterprises. Int J Prod Dev 2(1-2):71-84. https://doi.org/10.1504/IJPD.2005.006669

Githens G (2007) Product lifecycle management: Driving the next generation of lean thinking by Michael grieves. J Prod Innov Manag 24(3):278-280. https://doi.org/10.1111/j.1540-5885.2007.00250_2.x

Grieves M (2011) Virtually perfect: Driving innovative and lean products through product lifecycle management. Space Coast Press, Cocoa Beach

Tuegel EJ, Ingraffea AR, Eason TG, Spottswood SM (2011) Reengineering aircraft structural life prediction using a digital twin. Int J Aerosp Eng 2011:154798. https://doi.org/10.1155/2011/154798.

Dai S, Zhao G, Yu Y, Wang W (2018) Trend of digital product definition: from mock-up to twin. J Comput Aided Des Comput Graph 30(8):1554-1562. https://doi.org/10.3724/SP.J.1089.2018.16831

Li X, Liu X, Wan XX (2019) Overview of digital twins application and safe development. J Syst Simulat 31(3):385-392. https://doi.org/10.16182/j.issn1004731x.joss.19-0025

Cai YL, Gao X, Zhang Y (2019) Concept, method and application of digital twin technology. In: Proceedings of the 20th CCSSTA 2019, University of Science and Technology of China Press, Urumqi, 20 August 2019

Xiao L, Boyd S, Lall S (2005) A scheme for robust distributed sensor fusion based on average consensus. In: Proceedings of the IPSN 2005 Fourth International Symposium on Information Processing in Sensor Networks, IEEE, Boise, 15 April 2005. https://doi.org/10.1109/IPSN.2005.1440896

Cooper J, Noon M, Jones C, Kahn E, Arbuckle P (2013) Big data in life cycle assessment. J Ind Ecol 17(6):796-799. https://doi.org/10.1111/jiec.12069

Xia KL, Opron K, Wei GW (2013) Multiscale multiphysics and multidomain models - flexibility and rigidity. J Chem Phys 139(19):194109. https://doi.org/10.1063/1.4830404

Zhang CY, Tao F (2021) Evaluation index system for digital twin model. Comput Integr Manuf Syst 27(8):2171-2186. https://doi.org/10.13196/j.cims.2021.08.001

Tao F, Zhang CY, Qi QL, Zhang H (2022) Digital twin maturity model. Comput Integr Manuf Syst 28(5):1267-1281. https://doi.org/10.13196/j.cims.2022.05.001

Zhang H, Qi QL, Tao F (2022) A consistency evaluation method for digital twin models. J Manuf Syst 65:158-168. https://doi.org/10.1016/j.jmsy.2022.09.006

Kraft EM (2016) The air force digital thread/digital twin - life cycle integration and use of computational and experimental knowledge. In: Proceedings of the 4th AIAA Aerospace Sciences Meeting, AIAA, San Diego, 4–8 January 2016. https://doi.org/10.2514/6.2016-0897

Qi QL, Tao F, Zuo Y, Zhao DM (2018) Digital twin service towards smart manufacturing. Proc CIRP 72:237-242. https://doi.org/10.1016/j.procir.2018.03.103

Malik AA, Bilberg A (2018) Digital twins of human robot collaboration in a production setting. Procedia Manuf 17:278-285. https://doi.org/10.1016/j.promfg.2018.10.047

Xiao JH, Xie K, Chi JY (2019) Intelligent manufacturing, digital twin and strategic scenario modeling. J Beijing Jiaotong Univ: Soc Sci Ed 18(2):69-71. https://doi.org/10.3969/j.issn.1672-8106.2019.02.007

Tao F, Zhang M, Cheng JF, Qi QL (2017) Digital twin workshop: a new paradigm for future workshop. Comput Integr Manuf Syst 23(1):1-9. https://doi.org/10.13196/j.cims.2017.01.001

Tao F, Zhang M, Liu YS, Nee AYC (2018) Digital twin driven prognostics and health management for complex equipment. CIRP Ann 67(1):169-172. https://doi.org/10.1016/j.cirp.2018.04.055

Tao F, Cheng Y, Cheng JF, Zhang M, Xu WJ, Qi QL (2017) Theories and technologies for cyber-physical fusion in digital twin shop-floor. Comput Integr Manuf Syst 23(8):1603-1611. https://doi.org/10.13196/j.cims.2017.08.001

Tao F, Liu WR, Liu JH, Liu XJ, Liu Q, Qu T et al (2018) Digital twin and its potential application exploration. Comput Integr Manuf Syst 24(1):1-18. https://doi.org/10.13196/j.cims.2018.01.001

Tao F, Liu WR, Zhang M, Hu TL, Qi QL, Zhang H et al (2019) Five-dimension digital twin model and its ten applications. Comput Integr Manuf Syst 25(1):1-18. https://doi.org/10.13196/j.cims.2019.01.001

Tao F, Sui F, Liu A, Qi QL, Zhang M, Song BY et al (2019) Digital twin-driven product design framework. Int J Prod Res 57(12):3935-3953. https://doi.org/10.1080/00207543.2018.1443229

Li H, Tao F, Wang HQ, Song WY, Zhang ZF, Fan BB et al (2019) Integration framework and key technologies of complex product design-manufacturing based on digital twin. Comput Integr Manuf Syst 25(6):1320-1336. https://doi.org/10.13196/j.cims.2019.06.002

Xu LZ (2021) Research on dynamic scheduling of intelligent shop manufacturing process based on digital twin. Dissertation, Guizhou University

Yang F, Wu T, Liao RJ, Jiang JY, Chen T, Gao B (2021) Application and implementation method of digital twin in electric equipment. High Voltage Eng 47(5):1505-1521. https://doi.org/10.13336/j.1003-6520.hve.20210456

Li CZ, Mahadevan S, Ling Y, Choze S, Wang LP (2017) Dynamic Bayesian network for aircraft wing health monitoring digital twin. AIAA J 55(3):930-941. https://doi.org/10.2514/1.J055201

Bayer V, Kunath S, Niemeier R, Horwege J (2018) Signal-based metamodels for predictive reliability analysis and virtual testing. Adv Sci Technol Eng Syst J 3(1):342-347. https://doi.org/10.25046/aj030141

Millwater H, Ocampo J, Crosby N (2019) Probabilistic methods for risk assessment of airframe digital twin structures. Eng Fract Mech 221:106674. https://doi.org/10.1016/j.engfracmech.2019.106674

Omer M, Margetts L, Mosleh MH, Hewitt S, Parwaiz M (2019) Use of gaming technology to bring bridge inspection to the office. Struct Infrastruct Eng 15(10):1292-1307. https://doi.org/10.1080/15732479.2019.1615962

Shim CS, Dang NS, Lon S, Jeon CH (2019) Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model. Struct Infrastruct Eng 15(10):1319-1332. https://doi.org/10.1080/15732479.2019.1620789

Venkatesan S, Manickavasagam K, Tengenkai N, Vijayalakshmi N (2019) Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin. IET Electr Power Appl 13(9):1328-1335. https://doi.org/10.1049/iet-epa.2018.5732

Shcherba D, Tarasov A, Borovkov AI (2018) Developing of phenomenological damage model for automotive low-carbon structural steel for using in validation of euroncap frontal impact. Mater Phys Mech 40(2):246-253. https://doi.org/10.18720/MPM.4022018_13

Korostelkin AA, Klyavin OI, Aleshin MV, Wang GD, Wang SF, Liu JY (2019) Optimization of frame mass in crash testing of off-road vehicles. Russian Eng Res 39(12):1021-1028. https://doi.org/10.3103/S1068798X19120116

Liu Y, Zhang L, Yang Y, Zhou LF, Ren L, Wang F et al (2019) A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access 7:49088-49101. https://doi.org/10.1109/ACCESS.2019.2909828

Pizzolato C, Saxby DJ, Palipana D, Diamond LE, Barrett RS, Teng YD et al (2019) Neuromusculoskeletal modeling-based prostheses for recovery after spinal cord injury. Front Neurorobot 13:97. https://doi.org/10.3389/fnbot.2019.00097

Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18-23. https://doi.org/10.1016/j.mfglet.2014.12.001

Zhang YF, Dang Z, Shan R (2019) Survey on current research and future trends of smart manufacturing and its key technologies. Mech Sci Technol Aeros Eng 38(3):329-338. https://doi.org/10.13433/j.cnki.1003-8728.20180300

Zang CZ, Fan YS (2007) Complex event processing of manufacturing enterprises based on smart items. Comput Integr Manuf Syst 13(11):2243-2253

Liu JF, Zhou HG, Liu XJ, Tian GZ, Wu MF, Cao LP et al (2019) Dynamic evaluation method of machining process planning based on digital twin. IEEE Access 7:19312-19323. https://doi.org/10.1109/ACCESS.2019.2893309

Rauch L, Pietrzyk M (2019) Digital twins as a modern approach to design of industrial processes. J Mach Eng 19(1):86-97. https://doi.org/10.5604/01.3001.0013.0456

Yerra VA, Pilla S (2017) IIoT-enabled production system for composite intensive vehicle manufacturing. SAE Int J Engines 10(2):209-214. https://doi.org/10.4271/2017-01-0290

Dong YF, Tan RH, Zhang P, Peng QJ, Shao P (2021) Product redesign using functional backtrack with digital twin. Adv Eng Inform 49:101361. https://doi.org/10.1016/j.aei.2021.101361

Oyekan JO, Hutabarat W, Tiwari A, Grech R, Aung MH, Mariani MP et al (2019) The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans. Robot Com-Int Manuf 55:41-54. https://doi.org/10.1016/j.rcim.2018.07.006

Bilberg A, Malik AA (2019) Digital twin driven human–robot collaborative assembly. CIRP Ann 68(1):499-502. https://doi.org/10.1016/j.cirp.2019.04.011

Hu FW (2022) Mutual information-enhanced digital twin promotes vision-guided robotic grasping. Adv Eng Inform 52:101562. https://doi.org/10.1016/j.aei.2022.101562

Lee D, Lee S, Masoud N, Krishnan MS, Li VC (2022) Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction. Adv Eng Inform 53:101710. https://doi.org/10.1016/j.aei.2022.101710

Li H, Ma WF, Wang HQ, Liu G, Wen XY, Zhang YY et al (2022) A framework and method for human-robot cooperative safe control based on digital twin. Adv Eng Inform 53:101701. https://doi.org/10.1016/j.aei.2022.101701

Gupta A, Basu B (2019) Sustainable primary aluminium production: technology status and future opportunities. Transactions of the Indian Institute of Metals 72(8):2135-2150. https://doi.org/10.1007/s12666-019-01699-9

Llamas AA, Bartie NJ, Heibeck M, Stelter M, Reuter MA (2020) Simulation-based exergy analysis of large circular economy systems: zinc production coupled to CdTe photovoltaic module life cycle. J Sustain Metall 6:34-67. https://doi.org/10.1007/s40831-019-00255-5

Dai XT, Burns A (2020) Period adaptation of real-time control tasks with fixed-priority scheduling in cyber-physical systems. J Syst Architect 103:101691. https://doi.org/10.1016/j.sysarc.2019.101691

Arafsha F, Laamarti F, El Saddik A (2019) Cyber-physical system framework for measurement and analysis of physical activities. Electronics 8(2): 248. https://doi.org/10.3390/electronics8020248

Dong R, She CY, Hardjawana W, Li YH, Vucetic B (2019) Deep learning for hybrid 5g services in mobile edge computing systems: Learn from a digital twin. IEEE Trans Wirel Commun 18(10):4692-4707. https://doi.org/10.1109/TWC.2019.2927312

Söderberg R, Wärmefjord K, Carlson JS, Lindkvist L (2017) Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann 66(1):137-140. https://doi.org/10.1016/j.cirp.2017.04.038

Sierla S, Kyrki V, Aarnio P, Vyatkin V (2018) Automatic assembly planning based on digital product descriptions. Comput Ind 97:34-46. https://doi.org/10.1016/j.compind.2018.01.013

Fang YL, Peng C, Lou P, Zhou ZD, Hu JM, Yan JW (2019) Digital-twin-based job shop scheduling toward smart manufacturing. IEEE Trans Industr Inform 15(12):6425-6435. https://doi.org/10.1109/TII.2019.2938572

Longo F, Nicoletti L, Padovano A (2019) Ubiquitous knowledge empowers the smart factory: The impacts of a service-oriented digital twin on enterprises’ performance. Ann Rev Control 47:221-236. https://doi.org/10.1016/j.arcontrol.2019.01.001

Zhang HJ, Zhang GH, Yan Q (2018) Dynamic resource allocation optimization for digital twin-driven smart shopfloor. In: Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control, IEEE, Zhuhai, 27–29 March 2018. https://doi.org/10.1109/ICNSC.2018.8361283

Nikolaev S, Gusev M, Padalitsa D, Mozhenkov E, Mishin S, Uzhinsky I (2018) Implementation of “digital twin” concept for modern project-based engineering education. In: Chiabert P, Bouras A, Noël F, Ríos J (eds) Product lifecycle management to support industry 4.0, 15th IFIP WG 5.1 international conference, PLM 2018, Turin, Italy, July 2–4, 2018, IFIP Advances in Information and Communication Technology, vol 540. Springer, Cham, pp 193–203

Kim H, Shin H, Kim HS, Kim WT (2018) VR-CPES: A novel cyber-physical education systems for interactive VR services based on a mobile platform. Mob Inf Syst 2018:8941241. https://doi.org/10.1155/2018/8941241

Toivonen V, Lanz M, Nylund H, Nieminen H (2018) The FMS training center - a versatile learning environment for engineering education. Procedia Manuf 23:135-140. https://doi.org/10.1016/j.promfg.2018.04.006

Verner IM, Cuperman D, Reitman M (2017) Robot online learning to lift weights: A way to expose students to robotics and intelligent technologies. Int J Onl Eng 13:174. https://doi.org/10.3991/ijoe.v13i08.7270

Biglarbegian M (2018) High frequency Gan power converters digital twin. Dissertation, The University of North Carolina at Charlotte

Zhou MK, Yan JF, Feng DH (2019) Digital twin framework and its application to power grid online analysis. CSEE J Power Energy Syst 5(3):391-398. https://doi.org/10.17775/CSEEJPES.2018.01460

Zhou EZ, Feng DH, Yan JF, Zhao XX (2020) A software platform for second-order responsiveness power grid online analysis. Power Syst Technol 44(9):3474-3480. https://doi.org/10.13335/j.1000-3673.pst.2020.0083

He X, Ai Q, Zhu TY, Qiu CM, Zhang DX (2020) Opportunities and challenges of the digital twin in power system applications. Power Syst Technol 44(6):2009-2019. https://doi.org/10.13335/j.1000-3673.pst.2019.1983

Francisco A, Mohammadi N, Taylor JE (2020) Smart city digital twin-enabled energy management: Toward real-time urban building energy benchmarking. J Manag Eng 36(2):04019045. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000741

Kumar SAP, Madhumathi R, Chelliah PR, Tao L, Wang SG (2018) A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance. J Reliable Intell Environ 4(4):199-209. https://doi.org/10.1007/s40860-018-0069-y

Chen Y, Chen Y, Pei Z, Wang C (2020) Digital twin: Recent development and future trend from bibliometrics perspective. China Mech Eng 31(7):797-807. https://doi.org/10.3969/j.issn.1004-132X.2020.07.005