TY - JOUR
T1 - Digital twin-based opti-state control method for a synchronized production operation system
AU - Zhang, Kai
AU - Qu, Ting
AU - Zhou, Dajian
AU - Jiang, Hongfei
AU - Lin, Yuanxin
AU - Li, Peize
AU - Guo, Hongfei
AU - Liu, Yang
AU - Li, Congdong
AU - Huang, George Q.
N1 - Funding Information:
This work is supported by National Natural Science Foundation of China ( 51875251 ), 2018 Guangzhou Innovation Leading Talent Program, China ( 201909010006 ), Blue Fire Project (Huizhou) Industry-University-Research Joint Innovation Fund of Ministry of Education, China ( CXZJHZ201722 ), and the Fundamental Research Funds for the Central Universities, China ( 11618401 ).
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2020/6
Y1 - 2020/6
N2 - The intelligent manufacturing strategy and customer demand have mutually promoted each other. Also, the production mode is shifting towards customized production, and more rental resources or services are introduced to the production system, therefore, the systems are becoming more complex. Compared with traditional production systems, such systems have some new features, this work calls this type of system as a synchronized production operation system (SPOS). Under such circumstances, production systems are influenced by more frequent uncertainties, and the planning-based production decision and control approach is no longer applicable. The opti-state control (OsC) method is proposed to help SPOS keep in an optimal state when uncertainties affect the system. Besides, a digital twin-based control framework (DTCF) is designed for getting the full element information needed for decision making. Based on the comprehensive information of the production system obtained by the DTCF, the OsC method is introduced to the virtual control layer to formulate the optimal target guiding the path of the system in real time through the dynamic matching mechanism (qualitative perspective). Then multi-stage synchronized control with analysis target cascading (ATC) method is used to get the local optimal state decisions (quantitative perspective). From both qualitative and quantitative aspects to ensure the system is under an optimal target path for optimal operation procedure. At last, a case study in a large-size paint making company in China is used to validate the effectiveness of the approach.
AB - The intelligent manufacturing strategy and customer demand have mutually promoted each other. Also, the production mode is shifting towards customized production, and more rental resources or services are introduced to the production system, therefore, the systems are becoming more complex. Compared with traditional production systems, such systems have some new features, this work calls this type of system as a synchronized production operation system (SPOS). Under such circumstances, production systems are influenced by more frequent uncertainties, and the planning-based production decision and control approach is no longer applicable. The opti-state control (OsC) method is proposed to help SPOS keep in an optimal state when uncertainties affect the system. Besides, a digital twin-based control framework (DTCF) is designed for getting the full element information needed for decision making. Based on the comprehensive information of the production system obtained by the DTCF, the OsC method is introduced to the virtual control layer to formulate the optimal target guiding the path of the system in real time through the dynamic matching mechanism (qualitative perspective). Then multi-stage synchronized control with analysis target cascading (ATC) method is used to get the local optimal state decisions (quantitative perspective). From both qualitative and quantitative aspects to ensure the system is under an optimal target path for optimal operation procedure. At last, a case study in a large-size paint making company in China is used to validate the effectiveness of the approach.
KW - Digital Twin
KW - Optimal State
KW - Resilient Control
KW - Synchronized Control
KW - Uncertainties
UR - http://www.scopus.com/inward/record.url?scp=85075263269&partnerID=8YFLogxK
U2 - 10.1016/j.rcim.2019.101892
DO - 10.1016/j.rcim.2019.101892
M3 - Journal article
AN - SCOPUS:85075263269
SN - 0736-5845
VL - 63
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
M1 - 101892
ER -