Digital Twin-Based Production Workshop Efficiency Optimization
- DOI
- 10.2991/978-94-6463-256-9_126How to use a DOI?
- Keywords
- Digital twin; OEE; Production workshop; Support vector regression
- Abstract
As the main battlefield of the new generation of intelligent manufacturing, it is crucial to explore potential problems and optimize production efficiency of the production workshop. In this paper, we propose a dynamic and iterative method to optimize the efficiency of the production workshop by taking the Overall Efficiency of Equipment (OEE) as evaluation index. Firstly, we summarize the six major time losses affecting OEE into equipment abnormal state and human loss, which can be monitored and recorded in real time. Then, we construct an optimization platform that can update OEE in real time based on digital twin. Finally, taking monitoring the start-up operating temperature of an equipment as an example, the operation process and method library update of the platform are explained, and the iterative improvement of equipment production efficiency is realized. This study has reference significance for promoting the intelligent upgrading of the production workshop.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Weiyuan Wu PY - 2023 DA - 2023/10/09 TI - Digital Twin-Based Production Workshop Efficiency Optimization BT - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023) PB - Atlantis Press SP - 1236 EP - 1244 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-256-9_126 DO - 10.2991/978-94-6463-256-9_126 ID - Wu2023 ER -