Skip to main content
SpringerLink
Log in

Digital twin and its applications: A survey

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Digital twin is a technology that integrates multi-physical, multi-scale, and multi-disciplinary attributes. At the same time, it has the characteristics of real-time synchronization, faithful mapping, and high fidelity. It can realize the interaction and integration of physical world and information world. In recent years, digital twin technology has attracted the attention of academic and business circles, especially its application. Based on this background, this paper summarizes the concept and evolution of digital twin, studies the application tools and platforms of digital twin, and discusses the relationship between digital twin and related technologies. Then we focus on the application scenarios of digital twin, and summarize the new trends and requirements of digital twin application. The purpose of this paper is to provide reference for the practice of digital twin concept and technology in related fields in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

This paper is a theoretical study without relevant data.

Code availability

This paper is a theoretical study without relevant code.

References

  1. Saadaoui S, Tabaa M, Monteiro F, Chehaitly M, Dandache A (2019) Discrete wavelet packet transform-based industrial digital wireless communication systems[J]. Information (Switzerland) 10(3):104

    Google Scholar 

  2. Kapanen A (2019) The impact of Industry 4.0 on postgraduate industrial management education in Germany[C]. INTED2019 Proceedings, pp 7165–7172

  3. Wang F, Huang X (2018) Research on application- oriented electromechanical talents' training mode under background of "internet+ made in china 2025" promotion plan. Matter: Int J Sci Technol 4(2):172–181

  4. Tao F, Anwer N, Liu A, Wang L, Nee AY, Li L, Zhang M (2021) Digital twin towards smart manufacturing and industry 4.0[J]. J Manuf Syst 58:1–2

  5. Wang XV, Wang L (2019) Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0[J]. Int J Prod Res 57(11–12):3892 3902

  6. Mendi AF, Erol T, Doğan D (2021) Digital twin in the military field[J]. IEEE Internet Comput 26(5):33–40

  7. Wang Z, Feng W, Ye J, Yang J, Liu C (2021) A study on intelligent manufacturing industrial internet for injection molding industry based on digital twin[J]. Complexity p 16. https://doi.org/10.1155/2021/8838914

  8. Liu Datong, Guo Kai, Wang benkuan, et al (2018) Overview and prospect of digital twin technology [J]. J Instrum 39(11):1–10

  9. Madslien J (2018) Digital twins[J]. Prof Eng 31(5):28–29

  10. Tao F, Liu W, Liu J, Liu X, Liu Q, Qu T, Hu T, Zhang Z, Xiang F, Xu W, Wang J, Zhang Y, Liu Z, Li H, Cheng J, Qi Q, Zhang M, Zhang H, Sui F, He L, Yi W, Cheng H (2018) Digital twinning and its application exploration [J]. Comput Integr Manuf Syst 24(01):4–21

    Google Scholar 

  11. Barricelli BR, Casiraghi E, Fogli D (2019) A survey on digital twin: definitions, characteristics, applications, and design implications[J]. IEEE Access 7:167653–167671

    Article  Google Scholar 

  12. Yang linyao, Chen Siyuan, Wang Xiao, et al (2019) Digital twins and parallel systems: development status, comparison and prospect [J]. J Autom 45(11)

  13. Zhang Tianying, Ji Hang Overview of digital twins [C] 2019 China high level Forum on system simulation and virtual reality technology

  14. Minerva R, Lee GM, Crespi N (2020) Digital twin in the IoT context: a survey on technical features, scenarios, and architectural models[J]. Proc IEEE (99):1–40

  15. Tao F, Zhang H, Qi Q, Zhang M, Liu W, Cheng J, Ma X, Zhang L, Xue R (2020) Digital twin ten questions: analysis and thinking[J]. Comput Integr Manuf Syst 26(01):1–17

  16. Tao F, Zhang H, Liu A, Nee AYC (2019) Digital twin in industry: state-of-the-art[J]. IEEE Trans Industr Inf 15(4):2405–2415

    Article  Google Scholar 

  17. Zhou G, Zhang C, Li Z, Ding K, Wang C (2020) Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing[J]. Int J Prod Res 58

  18. Zhang C, Xu W, Liu J, Liu Z, Zhou Z, Pham DT (2019) A reconfigurable modeling approach for digital twin-based manufacturing system[J]. Procedia Cirp 83:118–125

  19. Tao F, Cheng Y, Cheng J, Zhang M, Xu W, Qi Q (2017) Theory and technologies for cyber-physical fusion in digital twin shop-floor[J]. Comput Integr Manuf Syst 23(8):1603–1611

  20. Grieves M (2014) Digital twin: manufacturing excellence through virtual factory replication[J]. White paper 1(2014):1–7

  21. Grieves MW (2011) Product lifecycle management: the new paradigm for enterprises[J]. Int J Prod Dev 2(1):1–8

    Google Scholar 

  22. Zhuang BC, Liu JH (2017) H Xiong Connotation, architecture and trends of product digital twin[J]. Comput Integr Manuf Syst 23(4):753–768

    Google Scholar 

  23. Tang T, Teng L (2018) J Wu Digitalization is the only way to intelligent manufacturing[J]. China Mech Eng 29(3):366–377

    Google Scholar 

  24. Tao F, Cheng Y, Cheng J, Zhang M, Xu W, Qi Q (2017) Theory and technologies for cyber-physical fusion in digital twin shop-floor[J]. Comput Integr Manuf Syst 23(8):1603–1611

  25. Tao F, Zhang M, Cheng JF (2017) Digital twin workshop:a new paradigm for future workshop[J]. Comput Integr Manuf Syst 23(1):1–9

    Google Scholar 

  26. Li M (2013) The design of desk lamp on 3D modeling based on Solidworks[J]. Int J Technol Manag 1:43–45

  27. Gao J, Sun B, Huo W (2021) Sensorless control of switched reluctance motor based on Matlab/Simulink Simulation[J]. J Phys Conf Ser 1813(01):012021. IOP Publishing

  28. (2018) Cloud-based cognitive premise security system using IBM Watson and IBM Internet of Things (IoT). Advances in Electronics, Communication and Computing

  29. González JD, Escobar JH, Sánchez H, De la Hoz J, Beltrán JR (2017) 2D and 3D virtual interactive laboratories of physics on Unity platform[J]. J Phys Conf 935:012069

  30. Paiva P, Freitas B, Carvalho LK, et al (2021) Online fault diagnosis for smart machines embedded in Industry 4.0 manufacturing systems: a labeled Petri net-based approach[J]. IFAC J Syst Control (3):100146

  31. Boschert S, Rosen R (2016) Digital twin-the simulation aspect[M]. Springer Verlag, Berlin, Germany

    Book  Google Scholar 

  32. Zhang M, Sui F, Liu A, Tao F, Nee A (2020) Digital twin driven smart product design framework[M]// Digital Twin Driven Smart Design

  33. Schleich B, Anwer N, Mathieu L (2017) Shaping the digital twin for design and production engineering[J]. CIRP Ann Manuf Technol 66(1):33–35

    Article  Google Scholar 

  34. Lu Y, Liu C, Wang IK, et al (2020) Digital Twin-driven smart manufacturing: connotation, reference model, applications and research issues[J]. Robot Comput Integr Manuf 61(Feb.):101837.1–101837.14

  35. Zhou G, Zhang C, Li Z, Ding K, Wang C (2020) Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing[J]. Int J Prod Res 58(4):1034–1051

  36. Tao F, Cheng Y, Cheng J et al (2017) Theory and technologies for cyber-physical fusion in digital twin shop-floor [J]. Comput Integr Manuf Syst 23(8):1603–1611

    Google Scholar 

  37. Sethy SP, Sameena T, Patil P, Shailaja K. Product life cycle management in pharmaceuticals: a review[J]. PHARMACEUTICALS: A REVIEW. Pharma tutor

  38. Ferguson S, Bennett E (2017) Digital twin tackles design challenges[J]. World Pumps 17(4):26–28

    Article  Google Scholar 

  39. Akhlaghi YG, Badiei A, Zhao X, Aslansefat K, Xiao X, Shittu S, Ma X (2020) A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins[J]. Energy Convers Manag pp 211–226

  40. Zhang X (2018) Design and implementation of workshop management and control system based on digital twins [D]. Zhengzhou University, Zhengzhou

    Google Scholar 

  41. Zou R, Liang X, Chen Q, Chen Q, Wang M, Zaghloul MAS, Lan H, Buric MP, Ohodnicki PR, Chorpening B, To AC, Chen KP (2020) A digital twin approach to study additive manufacturing processing using embedded optical fiber sensors and numerical modeling[J]. J Lightwave Technol 99:1–1

    Google Scholar 

  42. Lu Y, Liu C, Kevin I, Wang K, Huang H, Xu X (2020) Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot Comput Integr Manuf 61:101837

  43. Qi Q, Tao F (2018) Digital twin and big data towards smart manufacturing and Industry 4.0: 360 Degree Comparison[J]. IEEE Access 3585–3593

  44. Qi Q, Zhao D, Liao TW, Tao F (2018) Modeling of cyber-physical systems and digital twin based on edge computing, fog computing and cloud computing towards smart manufacturing. In: International Manufacturing Science and Engineering Conference 51357:V001T05A018. American Society of Mechanical Engineers

  45. Sameena T, Patil P, Shailaja K Product life cycle management in pharmaceuticals: a review[J]. Pharmatutor

  46. Liu Q, Leng J, Yan D, Zhang D, Chen X (2020) Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system[J]. J Manuf Syst 58:52–64

  47. Leng J, Liu Q, Ye S, Jing J, Wang Y, Zhang C, Zhang D, Chen X (2020) Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model[J]. 63

  48. Wang S, Kang X, Yu H, Wang Z (2020) Production line design and iterative evolution based on digital twin technology[J]. Mech Eng 8:28–30

    Google Scholar 

  49. Guerrero LV, López VV, Mejía JE (2014) Virtual commissioning with process simulation (Tecnomatix): computer-aided design and applications: Vol 11, No sup1[J]. Comput-Aided Des Applic.

  50. Aheleroff S, Zhong R, Xu X, Feng Z, Goyal P (2021) Digital twin enabled mass personalization: a case study of a smart wetland maintenance system[J]. International Manufacturing Science and Engineering Conference 84263:V002T07A025. American Society of Mechanical Engineers

  51. Huang S, Wang G, Yan Y, Fang X (2020) Blockchain-based data management for digital twin of product[J]. J Manuf Syst 54:361–371

  52. Dufour C, Soghomonian Z, Li W (2018) Hardware-in-the-loop testing of modern on-board power systems using digital twins[C]. 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) pp 118–123. IEEE

  53. Brosinsky C, Westermann D, Krebs R (2018) Recent and prospective developments in power system control centers: adapting the digital twin technology for application in power system control centers[C]. 2018 IEEE International Energy Conference (ENERGYCON) pp 1–6. IEEE

  54. Xie X, Parlikad AK, Puri RS (2019) A neural ordinary differential equations based approach for demand forecasting within power grid digital twins[C]. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) pp 1–6. IEEE

  55. Yan J, Zlatanova S, Aleksandrov M, Diakite AA, Pettit C (2019) Integration of 3D objects and terrain for 3d modelling supporting the digital twin[J].

  56. Zorich D (2010) Digital debates. WebWise Conference on Libraries and Museums in the Digital World Proceedings (10th, Capitol Hill, Washington, DC, February 25-27, 2009)[J]. Institute of Museum and Library Services

  57. Gerogiorgis DI, Castro-Rodriguez D (2021) A digital twin for process optimisation in Pharmaceutical Manufacturing[M]. Computer Aided Chemical Engineering 50:253–258. Elsevier

  58. Heaton J, Parlikad AK (2020) Asset information model to support the adoption of a digital twin: West Cambridge case study[J]. IFAC-PapersOnLine 53(3):366–371

    Article  Google Scholar 

  59. Bezborodova OE, Bodin ON, Gerasimov AI, Kramm MN, Rahmatullov RF, Ubiennykh AG (2020) Digital twin technology in medical information systems[J]. Journal of Physics: Conference Series 1515(5):052022. IOP Publishing

  60. Karakra A, Fontanili F, Lamine E, Lamothe J (2019) HospiT'Win: a predictive simulation-based digital twin for patients pathways in hospital[C]. 2019 IEEE EMBS international conference on biomedical & health informatics (BHI) pp 1–4. IEEE

  61. Oppermann S, Wirtz S, Schallhorn J, Moecke H (2003) The hospital as the scene of an emergency: evacuation procedures for in-hospital emergencies[J]. NOTFALL UND RETTUNGSMEDIZIN 6(8):591–595

  62. Peng Y, Zhang M, Yu F (2020) Digital twin hospital buildings: an exemplary case study through continuous lifecycle integration[J]. Advances in Civil Engineering pp 1–13

  63. Organization WH (2003) Background document: the diagnosis treatment and prevention of typhoid fever[J]. The Department of Vaccines and Biologicals: Geneva. 15(6):460–463

  64. Izzo Angelo AA (2018) PTR virtual issue on the experimental and clinical pharmacology of the nutraceutical curcumin[J]. Phytotherapy research: PTR 32(11):2107–2108

  65. Sinisi S, Alimguzhin V, Mancini T, Tronci E, Mari F, Leeners B (2020) Optimal personalised treatment computation through in silico clinical trials on patient digital twins [J]. Fund Inform 174(3–4):229

    MathSciNet  Google Scholar 

  66. Barricelli BR, Casiraghi E, Gliozzo J, Petrini A, Valtolina S (2020) Human digital twin for fitness management[J]. Ieee Access 8:26637–26664

  67. Lal A, Li G, Cubro E, Chalmers S, Gajic O (2020) Development and verification of a digital twin patient model to predict specific treatment response during the first 24 hours of sepsis[J]. Critical care explorations 2(11):e0249

  68. Angjeliu G, Coronelli D, Cardani G (2020) Development of the simulation model for digital twin applications in historical masonry buildings: the integration between numerical and experimental reality[J]. Comput Struct 238:106282

  69. Chao F A, Cheng Z A, Ay B, Am A (2019) Disaster city digital twin: a vision for integrating artificial and human intelligence for disaster management[J]. Int J Inf Manag 56

  70. Nochta T, Wan L, Schooling JM, Parlikad AK (2020). A Socio-technical perspective on urban analytics: the case of city-scale digital twins[J]. J Urban Technol (4)

  71. Bao J, Guo D, Jie L, Zhang J (2018) The modelling and operations for the digital twin in the context of manufacturing[J]. Enterprise Information Systems 13(4):534–556

  72. White G, Zink A, Lara Codecà, Clarke S (2021) A digital twin smart city for citizen feedback[J]. Cities 110

  73. Du J, Zhu Q, Shi Y, Wang Q, Lin Y, Zhao D (2019) Cognition digital twins for personalized information systems of smart cities: proof of concept[J]. J Manag Eng 36(2)

  74. Ahn C, Ham Y, Kim J, Kim J (2020) A digital twin city model for age-friendly communities: capturing environmental distress from multimodal sensory data[C]// Hawaii International Conference on System Sciences.

  75. Agnusdei GP, Elia V, Gnoni MG (2021) A classification proposal of digital twin applications in the safety domain[J]. Comput Ind Eng 154(5):107137

    Article  Google Scholar 

  76. Zhao L, Han G, Li Z, Shu L (2020) Intelligent digital twin-based software-defined vehicular networks[J]. IEEE Network 34(5):178–184

  77. Laryukhin V, Skobelev P, Lakhin O, Grachev S, Yalovenko O (2019) Towards developing a cyber-physical multi-agent system for managing precise farms with digital twins of plants[J]. Cybernetics and Physics 8(4):257–261

  78. Monteiro J, Barata J, Veloso M, Veloso L, Nunes J (2019) Towards sustainable digital twins for vertical farming[J]. Thirteenth International Conference on Digital Information Management (ICDIM) pp 234–239. IEEE

  79. Yun S, Park JH, Kim WT (2017) Data-centric middleware based digital twin platform for dependable cyber-physical systems[C]. 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN) pp 922–926. IEEE

  80. Alves RG, Souza G, Maia RF, Tran ALH, Kamienski C, Soininen J, Aquino PT, Lima F (2019) A digital twin for smart farming[C]. 2019 IEEE Global Humanitarian Technology Conference (GHTC) pp 1–4. IEEE

  81. Erdélyi V, Jánosi L (2019) Digital twin and shadow in smart pork fetteners[J]. International Journal of Engineering and Management Sciences 4(1):515–520

  82. Schroeder GN, Steinmetz C, Pereira CE, Espindola DB (2016) Digital twin data modeling with AutomationML and a communication methodology for data exchange[J]. IFAC-Pap Line 49(30):12–17

  83. Tao F, Cheng J, Qi Q (2017) IIHub: an industrial Internet-of-Things hub towards smart manufacturing based on cyber-physical system[J]. IEEE Trans Ind Inf 1–1

  84. Coronado PDU, Lynn R, Louhichi W, Parto M, Wescoat E, Kurfess T (2018) Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system[J]. J Manuf Syst 48:25–33

  85. Tao F, Zhang M (2017) Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing[J]. Ieee Access 5:20418–20427

  86. Tao F, Zhang M, Liu Y et al (2018) Digital twin driven prognostics and health management for complex equipment[J]. Cirp Annals 67(1):169–172

  87. Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing[J]. J Manuf Syst 48:157–169

  88. Romelfanger M, Kolich M (2019) Comfortable automotive seat design and big data analytics: a study in thigh support[J]. Appl Ergon 75:257–262

    Article  Google Scholar 

  89. Yan J, Meng Y, Lu L, Guo C (2017) Big-data-driven based intelligent prognostics scheme in industry 4.0 environment[C] Prognostics and System Health Management Conference (PHM-Harbin).

  90. Rado O, Lupia B, Leung JMY, Kuo YH, Graham CA (2014) Using simulation to analyze patient flows in a hospital emergency department in Hong Kong[C]. Proceedings of the International Conference on Health Care Systems Engineering. Springer, Cham, pp 289–301

Download references

Funding

This work received financial support from the Tianjin Municipal Education Commission (grant no. 2017KJ057).

Author information

Authors and Affiliations

  1. College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, 300222, China

    Rui Zhang, Fang Wang & Jingsha Zheng

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Jun Cai

  3. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin, 300384, China

    Yan Wang

  4. Institute of Physical Internet, Jinan University, Zhuhai, 519070, China

    Hongfei Guo

  5. School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, 519070, China

    Hongfei Guo

Authors
  1. Rui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongfei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jingsha Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Thesis revision was provided by Rui Zhang, writing assistance was provided by Fang Wang, and the drawing was done by Yan Wang, Hongfei Guo, and Jingsha Zheng.

Corresponding author

Correspondence to Jun Cai.

Ethics declarations

Ethics approval

There is no moral problem in this paper.

Consent to participate

All the authors agree to participate.

Consent for publication

All the authors agree for publication this paper.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, R., Wang, F., Cai, J. et al. Digital twin and its applications: A survey. Int J Adv Manuf Technol 123, 4123–4136 (2022). https://doi.org/10.1007/s00170-022-10445-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10445-3

Keywords

Search

Navigation