TY - GEN
T1 - A Novel Bionic Digital Twin-Based Manufacturing System Toward the Mass Customization Paradigm
AU - Liu, Shimin
AU - Zheng, Pai
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/8
Y1 - 2023/8
N2 - Introducing the digital twin into the manufacturing system has formed an intelligent manufacturing system with paired physical entities and digital models, which can monitor and control the entire manufacturing process. With the acceleration of Internet-driven product research and development, the manufacturing demand for mass customization products is shifting towards high accuracy, high complexity, and multiple varieties. The dynamic characteristics of manufacturing processes are not considered, and the uncertainty of manufacturing processes has not yet been discussed. The lack of the above research points has seriously affected the robustness of digital twin-based manufacturing systems, making it difficult to adapt to these growing demands for small-batch customization. In this paper, a novel digital twin-based manufacturing system is proposed, which endows the digital twin with bionic characteristics to improve the system's adaptability. During the manufacturing process, the system adaptively changes to meet the changing manufacturing requirements. In the context of dynamically changing production requirements, the mutual transformation between knowledge and algorithms is required to achieve self-evolution of system performance. It is believed that introducing the bionic concept can enable digital twin-based manufacturing systems to adapt to mass customization paradigms quickly, ensuring product quality and manufacturing efficiency.
AB - Introducing the digital twin into the manufacturing system has formed an intelligent manufacturing system with paired physical entities and digital models, which can monitor and control the entire manufacturing process. With the acceleration of Internet-driven product research and development, the manufacturing demand for mass customization products is shifting towards high accuracy, high complexity, and multiple varieties. The dynamic characteristics of manufacturing processes are not considered, and the uncertainty of manufacturing processes has not yet been discussed. The lack of the above research points has seriously affected the robustness of digital twin-based manufacturing systems, making it difficult to adapt to these growing demands for small-batch customization. In this paper, a novel digital twin-based manufacturing system is proposed, which endows the digital twin with bionic characteristics to improve the system's adaptability. During the manufacturing process, the system adaptively changes to meet the changing manufacturing requirements. In the context of dynamically changing production requirements, the mutual transformation between knowledge and algorithms is required to achieve self-evolution of system performance. It is believed that introducing the bionic concept can enable digital twin-based manufacturing systems to adapt to mass customization paradigms quickly, ensuring product quality and manufacturing efficiency.
UR - http://www.scopus.com/inward/record.url?scp=85174391948&partnerID=8YFLogxK
U2 - 10.1109/CASE56687.2023.10260636
DO - 10.1109/CASE56687.2023.10260636
M3 - Conference article published in proceeding or book
AN - SCOPUS:85174391948
T3 - IEEE International Conference on Automation Science and Engineering
SP - 6
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Y2 - 26 August 2023 through 30 August 2023
ER -