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A Survey of Digital Twin for Industry 4.0: Benefits, Challenges and Opportunities

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Abstract

Manufacturing processes are becoming increasingly digital. As this trend unfolds, many companies often struggle to determine what they should be doing to drive and deliver real value both operationally and strategically. The Digital Twin (DT) is a virtual representation of a physical object, which has been proposed as one of the key concepts for Industry 4.0. The DT provides a virtual representation of products along their lifecycle that enables the prediction and optimization of the behavior of a production system and its components. A digital twin can model as a composition of basic components that provide basic functionalities, such as identification, storage, communication, security, data management, Human Machine Interface (HMI) and simulation. Recently new advanced machine-learning algorithms, data visualization, and simulation techniques have created new opportunities for reducing costs and aggregating value to the production chain. Also Cyber Physical Systems (CPSs) have been proposed as a key concept of Industry 4.0 architectures. The CPS can be described as a set of physical devices, objects and equipment that interacts with virtual cyberspace through a communication network. The cyber model of each physical entity is seen as a digital representation of the real entity and as such, sometimes called the digital twin. The digital twin and CPS are gaining popularity due to their significant impacts on the realization of smart manufacturing within which data and information from different systems is used to increase self-awareness, self-predict, and self-configure functionalities.

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Acknowledgements

The authors would like to thank all who are directly or indirectly involved, advised and coordinated in collecting data and software information for the support of this paper.

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Authors and Affiliations

  1. Department of AIML, BNM Institute of Technology, Banashankri, Bengaluru, Karnataka, 560064, India

    Tejaswini R. Murgod & U. Mahanthesha

  2. Department of Artificial Intelligence & Data Science, NITTE Meenakshi Institute of Technology, Bangalore, Karnataka, 560064, India

    S. Meenakshi Sundaram

  3. Department of CSE-Honours, KL University, Vijayawada, India

    Pandiyanathan Murugesan

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  1. Tejaswini R. Murgod
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  2. S. Meenakshi Sundaram
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  3. U. Mahanthesha
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Correspondence to Tejaswini R. Murgod.

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Murgod, T.R., Sundaram, S.M., Mahanthesha, U. et al. A Survey of Digital Twin for Industry 4.0: Benefits, Challenges and Opportunities. SN COMPUT. SCI. 5, 76 (2024). https://doi.org/10.1007/s42979-023-02363-2

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