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A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives

Author

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  • Kendrik Yan Hong Lim

    (Nanyang Technological University
    Nanyang Technological University)

  • Pai Zheng

    (Nanyang Technological University
    Nanyang Technological University
    The Hong Kong Polytechnic University)

  • Chun-Hsien Chen

    (Nanyang Technological University
    Nanyang Technological University)

Abstract

With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.

Suggested Citation

  • Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
  • Handle: RePEc:spr:joinma:v:31:y:2020:i:6:d:10.1007_s10845-019-01512-w
    DOI: 10.1007/s10845-019-01512-w
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    1. Georgios Falekas & Athanasios Karlis, 2021. "Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects," Energies, MDPI, vol. 14(18), pages 1-26, September.
    2. Sebastian Lawrenz & Benjamin Leiding & Marit Elke Anke Mathiszig & Andreas Rausch & Mirco Schindler & Priyanka Sharma, 2021. "Implementing the Circular Economy by Tracing the Sustainable Impact," IJERPH, MDPI, vol. 18(21), pages 1-13, October.
    3. Leung, Eric K.H. & Lee, Carmen Kar Hang & Ouyang, Zhiyuan, 2022. "From traditional warehouses to Physical Internet hubs: A digital twin-based inbound synchronization framework for PI-order management," International Journal of Production Economics, Elsevier, vol. 244(C).
    4. Remigiusz Iwańkowicz & Radosław Rutkowski, 2023. "Digital Twin of Shipbuilding Process in Shipyard 4.0," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    5. Lan, Lan & Zhou, Zhifang, 2024. "Complementary or substitutive effects? The duality of digitalization and ESG on firm's innovation," Technology in Society, Elsevier, vol. 77(C).
    6. Yujie Ma & Xueer Chen & Shuang Ma, 2024. "Optimal Sustainable Manufacturing for Product Family Architecture in Intelligent Manufacturing: A Hierarchical Joint Optimization Approach," Sustainability, MDPI, vol. 16(7), pages 1-27, March.
    7. Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    8. João Vieira & João Poças Martins & Nuno Marques de Almeida & Hugo Patrício & João Gomes Morgado, 2022. "Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    9. Paula Morella & María Pilar Lambán & Jesús Royo & Juan Carlos Sánchez & Jaime Latapia, 2023. "Technologies Associated with Industry 4.0 in Green Supply Chains: A Systematic Literature Review," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    10. Mohammed M. Mabkhot & Pedro Ferreira & Antonio Maffei & Primož Podržaj & Maksymilian Mądziel & Dario Antonelli & Michele Lanzetta & Jose Barata & Eleonora Boffa & Miha Finžgar & Łukasz Paśko & Paolo M, 2021. "Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    11. Fuwen Hu & Xianjin Qiu & Guoye Jing & Jian Tang & Yuanzhi Zhu, 2023. "Digital twin-based decision making paradigm of raise boring method," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2387-2405, June.
    12. Fromhold-Eisebith, Martina & Marschall, Philip & Peters, Robert & Thomes, Paul, 2021. "Torn between digitized future and context dependent past – How implementing ‘Industry 4.0’ production technologies could transform the German textile industry," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    13. Lim, Kendrik Yan Hong & Dang, Le Van & Chen, Chun-Hsien, 2024. "Incorporating supply and production digital twins to mitigate demand disruptions in multi-echelon networks," International Journal of Production Economics, Elsevier, vol. 273(C).
    14. SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.
    15. Angelo Corallo & Vito Del Vecchio & Marianna Lezzi & Paola Morciano, 2021. "Shop Floor Digital Twin in Smart Manufacturing: A Systematic Literature Review," Sustainability, MDPI, vol. 13(23), pages 1-24, November.
    16. Saporiti, Nicolò & Cannas, Violetta Giada & Pozzi, Rossella & Rossi, Tommaso, 2023. "Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study," International Journal of Production Economics, Elsevier, vol. 261(C).
    17. Gurtej Singh Saini & AmirHossein Fallah & Pradeepkumar Ashok & Eric van Oort, 2022. "Digital Twins for Real-Time Scenario Analysis during Well Construction Operations," Energies, MDPI, vol. 15(18), pages 1-22, September.
    18. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    19. Weifei Hu & Jinyi Shao & Qing Jiao & Chuxuan Wang & Jin Cheng & Zhenyu Liu & Jianrong Tan, 2023. "A new differentiable architecture search method for optimizing convolutional neural networks in the digital twin of intelligent robotic grasping," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2943-2961, October.
    20. Zhicheng Xu & Vignesh Selvaraj & Sangkee Min, 2024. "State identification of a 5-axis ultra-precision CNC machine tool using energy consumption data assisted by multi-output densely connected 1D-CNN model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 147-160, January.
    21. Maksim Dli & Andrei Puchkov & Valery Meshalkin & Ildar Abdeev & Rail Saitov & Rinat Abdeev, 2020. "Energy and Resource Efficiency in Apatite-Nepheline Ore Waste Processing Using the Digital Twin Approach," Energies, MDPI, vol. 13(21), pages 1-13, November.
    22. Yanzhi Zhao & Mingsi Zhao & Fengyu Shi, 2024. "Integrating Moral Education and Educational Information Technology: A Strategic Approach to Enhance Rural Teacher Training in Universities," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 15053-15093, September.
    23. Madalina CUC, 2021. "Improving The Decision-Making Process By Modeling Digital Twins In A Big Data Environment," Management and Marketing Journal, University of Craiova, Faculty of Economics and Business Administration, vol. 0(1), pages 138-154, May.
    24. Fabrizio Banfi & Raffaella Brumana & Graziano Salvalai & Mattia Previtali, 2022. "Digital Twin and Cloud BIM-XR Platform Development: From Scan-to-BIM-to-DT Process to a 4D Multi-User Live App to Improve Building Comfort, Efficiency and Costs," Energies, MDPI, vol. 15(12), pages 1-26, June.
    25. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.

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