Digital twin-based opti-state control method for a synchronized production operation system

Abstract

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are influenced by more frequent uncertainties, and the planningbased production decision and control approach is no longer applicable.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 Kai Zhang a,c , Ting Qu b,c,⁎

Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

“…By extending the classical digital twin mapping system [22][23][24][25][26] to a digital twin control system which meets the requirements of dynamic evaluation of all factors, we build the digital-twin-driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle as shown in Fig. 2.…”

“…I1 0 0 0 1 0.7506 0.7506 1 0.7506 0.7506 The PRI of one node relative to another node is obtained according to Formula (21) and is shown in Table 18. Here, As shown in Table 19, the outflow, inflow, net flow of a node in IndN are obtained according to Formulas (22)- (24), then the net flow of all nodes are normalized and lastly the importance degree of the indicator corresponding to each node is obtained according to Formula (25). Based on the indicator value shown in Table 10 and indicator importance degree shown in Table 19, the twostage evidence combination (Fig.…”

“…Tao et al [23,24] summarized the progress in the application and theoretical research of digital twin in enterprises, put forward a five dimension structure model of digital twin and six application criteria of digital twin driving, and explored 14 types of key problems and technologies needed to be solved in the process of application conceiving and implementing driven by digital twin. In the aspect of digital twin framework research, Zhang et al [25] proposed the optimal state control (OsC) method to help synchronized production operation system (SPOS) keep in an optimal state when uncertainties affect the system and designed a digital twin-based control framework (DTCF) for getting the full element information needed for decision making. Li et al [26] proposed a digital twin-driven framework to enhance the optimization of product-service system (PSS) scheme selection.…”

“…Digital twin can build a real-time complete model of the physical layer in the virtual layer, which provides a suitable information framework. Based on the prior researches [25,26], the benchmarks of digital twin framework are abstracted as follows. The framework can mainly be divided into two layers: physical object layer and virtual decision/control layer.…”

“…The decision/control mechanism should be imported to this layer at the same time, which makes the dynamical decision/control possible. Aiming at the problem that production execution information and machine tool operation information have different sources, and the integration of the two types of information is difficult, Coronado et al [25] proposed a new manufacturing execution system, which uses data to build a digital twin workshop for production control and optimization. Banerjee et al [26] combined knowledge learning with digital twin, and proposed a method to extract and infer knowledge from large-scale production line data and improve manufacturing process management through reasoning ability.…”