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Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing
Open accessAbstract
This paper presents sensor data integration and information fusion to build “digital-twins” virtual machine tools for cyber-physical manufacturing. Virtual machine tools are useful for simulating machine tools’ capabilities in a safe and cost-effective way, but it is challenging to accurately emulate the behavior of the physical tools. When a physical machine tool breaks down or malfunctions, engineers can always go back to check the digital traces of the “digital-twins” virtual machine for diagnosis and prognosis. This paper presents an integration of manufacturing data and sensory data into developing “digital-twins” virtual machine tools to improve their accountability and capabilities for cyber-physical manufacturing. The sensory data are used to extract the machining characteristics profiles of a digital-twins machine tool, with which the tool can better reflect the actual status of its physical counterpart in its various applications. In this paper, techniques are discussed for deploying sensors to capture machine-specific features, and analytical techniques of data and information fusion are presented for modeling and developing “digital-twins” virtual machine tools. Example of developing the digital-twins of a 3-axis vertical milling machine is presented to demonstrate the concept of modeling and building a digital-twins virtual machine tool for cyber-physical manufacturing. The presented technique can be used as a building block for cyber-physic manufacturing development.
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Citations
Cited by
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Digital Twin in manufacturing: A categorical literature review and classification
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.474 · Cited by 3,612
“While some researchers, especially coming from the class Digital Shadow, see a further research need in developing the optimization and simulation methods, others see the data connectivity as main problem.(Yang et al. 2016; Um et al. 2017b; Vachálek et al. 2017; Schleich et al. 2017; Frazzon et al. 2016; Kuhn 2017; St…”
BackgroundDigital twin: Enabling technologies, challenges and open research
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998358 · Cited by 2,965
Characterising the Digital Twin: A systematic literature review
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020) · CIRP Journal of Manufacturing Science and Technology · doi:10.1016/j.cirpj.2020.02.002 · Cited by 2,422
Enabling technologies and tools for digital twin
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579
Metadata
- Authors
- Cai, Y., Starly, B., Cohen, P., & Lee, Y.-S.
- Venue
- Procedia Manufacturing
- Publisher
- Elsevier BV
- Published
- 2017
- Volume
- 10
- Pages
- 1031-1042
- ISSN
- 2351-9789 (ISSN-L)
- OpenAlex
- W2727938846
- Cited by
- 340
- References
- 0
- FWCI
- 23.09
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Cai, Y., Starly, B., Cohen, P., & Lee, Y.-S. (2017). Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing. Procedia Manufacturing. https://doi.org/10.1016/j.promfg.2017.07.094
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