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Visual analytics for digital twins: a conceptual framework and case study

Author

Listed:
  • Hangbin Zheng

    (Donghua University)

  • Tianyuan Liu

    (The Hong Kong Polytechnic University)

  • Jiayu Liu

    (Donghua University)

  • Jinsong Bao

    (Donghua University)

Abstract

The new generation of intelligent manufacturing systems requires a deep integration of human-cyber-physical spaces. Visual analytics plays a critical role in effectively navigating humans through twin data, enabling them to make better decisions and discover new knowledge, contributing to the evolution of digital twins. However, due to the lack of study of visual analytics components and their inter-relations in the context of the digital twin, the visual analytic process of digital twin systems lacks guidance to fully integrate human domain experience and cognitive abilities into the intelligent decision-making process of digital twin systems. Therefore, this paper proposes a conceptual visual analytics framework for digital twins (DTVA). Moreover, from the perspective of data and models in digital twins, the works concerning key factors of visual analytics, including digital-twin visual representation (DTV), analytical reasoning (DTA), and human–machine interface (DTI), are analyzed from data flow, task flow, and visual flow. At last, a case study of DTVA-based anomaly detection, which focuses on the abnormality detection of crane operation status, is presented. This work can support digital twin visual analytics, put forward ideas for the direction of visual analytics, and provide a promising direction for digital twin visualization services.

Suggested Citation

  • Hangbin Zheng & Tianyuan Liu & Jiayu Liu & Jinsong Bao, 2024. "Visual analytics for digital twins: a conceptual framework and case study," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1671-1686, April.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02135-y
    DOI: 10.1007/s10845-023-02135-y
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    References listed on IDEAS

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    1. Kamil Židek & Ján Piteľ & Milan Adámek & Peter Lazorík & Alexander Hošovský, 2020. "Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    2. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    3. Peng Zhan & Shaokun Wang & Jun Wang & Leigang Qu & Kun Wang & Yupeng Hu & Xueqing Li, 2021. "Temporal anomaly detection on IIoT-enabled manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1669-1678, August.
    4. Zenggui Gao & Jiaying Li & Mengyao Dong & Ruining Yang & Lilan Liu, 2022. "Human–System Interaction Based on Eye Tracking for a Virtual Workshop," Sustainability, MDPI, vol. 14(11), pages 1-18, June.
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