Integrating Virtual Twin and Deep Neural Networks for Efficient and Energy-Aware Robotic Deburring in Industry 4.0
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12541-023-00875-8.pdf
Reference50 articles.
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3. Kim, S. W., Kong, J. H., Lee, S. W., & Lee, S. (2022). Recent advances of artificial intelligence in manufacturing industrial sectors: A review. International Journal of Precision Engineering and Manufacturing, 23, 111–129. https://doi.org/10.1007/s12541-021-00600-3
4. Qi, Q., Tao, F., Hu, T., et al. (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58, 3–21. https://doi.org/10.1016/j.jmsy.2019.10.001
5. Caesar, B., Hanel, A., Wenkler, E., et al. (2020). Information model of a digital process twin for machining processes. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1765–1772).Vienna, Austria: IEEE.
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