This work studies a digital twin online analytics for throughput improvement of assembly flow line. As the representation of the physical assembly flow line, the proposed method includes two digital twin models to analyze the online data collected from the physical line and to calculate and apply optimal throughput improvement scheme to the physical line. With the proposed method, the online data could be fully utilized to serve the physical line, and the dependency on the experience of onsite engineers is greatly reduced. Two practical assembly lines are equipped with the proposed digital twin online analytics to demonstrate its effectiveness and efficiency.

Throughput, Workstations, Predictive models, Analytical models, Computational modeling, Data models, Layout

H. Sun, C. Li, X. Fang, H. Gu, "Optimized throughput improvement of assembly flow line with digital twin online analytics," in Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, Macao, Dec 05–08, 2017, pp. 1833–1837.

@INPROCEEDINGS{8324685,
author={Sun, Heqing and Li, Cheng and Fang, Xinyu and Gu, Hao},
booktitle={2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)},
title={Optimized throughput improvement of assembly flow line with digital twin online analytics},
year={2017},
volume={},
number={},
pages={1833-1837},
keywords={Throughput;Workstations;Predictive models;Analytical models;Computational modeling;Data models;Layout;optimized throughput improvement;throughput prediction;digitial twin;assembly flow line},
doi={10.1109/ROBIO.2017.8324685}
}