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Special Issue: Digital Twin-enabled Smart Industrial Systems

International Journal of Computer Integrated Manufacturing

Deadline: 31 March 2019

A digital twin refers to a virtual representation of a physical asset or system throughout its lifecycle [1]. It leverages the Internet of Things (IoT) to facilitate learning, reasoning and improving decision-making in complex systems [1]. The adoption of digital twin technologies is becoming more prevalent in the manufacturing industry, including product design, maintenance, and production planning [2-4].  Its concept has also led to a variety of emerging applications in other industries, such as energy [5], transportation [6], and healthcare [7]. The core of the technologies consists of artificial intelligence algorithms, enabled by machine learning methods trained on massive amounts of data captured through numerous connected sensors on the physical objects. Computer simulations are run to examine different possible scenarios to predict the outcomes of decisions. For timely interaction, digital twins evolve and continuously update to reflect changes in their physical counterparts [8]. The convergence between the physical assets and the virtual greatly determines the success of the applications [9,10]. The ultimate goal of digital twins is to drive systems smarter and more efficient.

While digital twin technologies have shown promising benefits, there are still challenges on their implementations, for example, synchronization between physical assets and virtual space, optimization and coordination on connected networks, dealing with uncertainty in systems, and development of computationally efficient algorithms for real-time response. New mathematical modeling techniques and solution methodologies, powered by new types of auto-sensed data, are needed to tackle the challenges. The emerge of digital twin technologies has presented new research opportunities.

International Journal of Computer Integrated Manufacturing

Language: en-US

Publisher: tandf

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Scope of the Special Issue

The purposes of this Special Issue of the International Journal of Computer Integrated Manufacturing are to present the state-of-the-art research on smart industrial systems enabled by digital twin technologies, demonstrate the benefits of the adoption of the technologies in complex systems, and to anticipate the potential challenges.

We invite submissions that present original and high-quality research work on digital twin-enabled smart industrial systems. We consider submissions that introduce new research problems and concepts, develop novel and rigorous methodologies to tackle the problems, and present innovative applications. Successful real-world implementations are strongly encouraged. This Special Issue is associated with the APIEMS 2018 Conference. However, non-participants are welcome to contribute to the Special Issue as well.

Submission guidelines

  1. The submission should conform to guidelines of the International Journal of Computer Integrated Manufacturing, which are available at: https://www.tandfonline.com/action/authorSubmission?journalCode=tcim20&page=instructions
  2. Manuscript shall be submitted via https://mc.manuscriptcentral.com/tcim.
  3. Only papers describing previously unpublished, original, state-of-the-art research, and not currently under review by a conference or a journal will be considered.
  4. In the submission system, the authors need to indicate that the paper is for the special issue “Digital Twin-enabled Smart Industrial Systems”.
  5. The title of the paper on the system should start with (Digital Twin). An example title would thus be “(Digital Twin) Digital Twins for Smart Manufacturing in the aerospace industry”.
  6. All the submission to the Special Issue will be handled according to the Journal’s standard review procedure

Editorial information

  • Guest Editor: Yong-Hong Kuo, Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong (yhkuo@hku.hk)
  • Guest Editor: Francesco Pilati, Department of Industrial Engineering University of Bologna(francesco.pilati3@unibo.it)
  • Guest Editor: Ting Qu , School of Electrical and Information Engineering Jinan University(quting@jnu.edu.cn)
  • Guest Editor: George Q. Huang, Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong (gqhuang@hku.hk)