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Journal of Intelligent Manufacturing

Erschienen in:

25.04.2023

A framework and method for equipment digital twin dynamic evolution based on IExATCN

verfasst von: Kunyu Wang, Lin Zhang, Zidi Jia, Hongbo Cheng, Han Lu, Jin Cui

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2024

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Abstract

Dynamic evolution is the most typical feature of a digital twin, making it different from a traditional digital model. Dynamic evolution is also the core technology for building equipment digital twins because it ensures consistency between physical space and virtual space. This paper proposes a dynamic evolution framework for black box equipment digital twins. The framework consists of three main parts: data acquisition and processing, an evolution triggering mechanism and an evolution algorithm. A formal description of the dynamic evolution of a black box digital twin is also given. Furthermore, by synthetically considering the computational accuracy and efficiency, we design an incremental external attention temporal convolution network (IExATCN) model to instantiate the proposed framework. Finally, the significance of digital twin dynamic evolution and the effectiveness of the IExATCN is verified by 3D equipment attitude estimation datasets.

Vorheriger Artikel Anticipatory analysis of AGV trajectory in a 5G network using machine learning

Nächster Artikel A novel 2.5D machining feature recognition method based on ray blanking algorithm

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Titel: A framework and method for equipment digital twin dynamic evolution based on IExATCN
verfasst von: Kunyu Wang
Lin Zhang
Zidi Jia
Hongbo Cheng
Han Lu
Jin Cui
Publikationsdatum: 25.04.2023
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2024
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-023-02125-0

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