As an efficient way to realize seamless interaction, integration, and fusion between the physical space and the virtual space for manufacturing, the concept of digital twin shop-floor (DTS) was proposed in Chapter 4, Digital Twin Shop-Floor. In this chapter, cyberphysical fusion in the DTS is studied from four aspects, including fusion of physical elements in physical shop-floor (PS), model fusion in virtual shop-floor (VS), data fusion in shop-floor digital twin data (SDTD), and fusion of services in shop-floor service systems (SSS). Related criteria and key technologies for these fusions are also discussed.
To adapt to the manufacturing needs and trends such as socialization, personalization, servitization, intelligence, and greenization, various countries have proposed different national-level manufacturing development strategies as mentioned in Chapter 4, Digital Twin Shop-Floor. One of the common goals of these strategies is to realize cyberphysical fusion, and consequently to achieve smart manufacturing. On the shop-floor, which is the basic “playground” of manufacturing activities, digital twin shop-floor (DTS) has been proposed. For the DTS, cyberphysical fusion is not only the goal, but also one of the core challenges in practice . To realize this fusion, it is essential to implement fusion in the major components of the DTS, respectively. These four processes include fusion of heterogeneous physical elements on the physical shop-floor (PS), fusion of multidimensional models on the virtual shop-floor (VS), cyberphysical data fusion on the shop-floor digital twin data (SDTD), and services fusion on the shop-floor service system (SSS). Some existing works have laid foundations for the above fusions, including connection and interconnection on the shop-floor [2,3], digital/ virtual shop-floor modeling [4,5], shop-floor data and information integration [6,7], shop-floor optimal operations and precision management. etc. Based on this, many valuable theories and technologies have been developed to solve the problem of fusion at the shop-floor level from different perspectives. However, to realize the fusion completely, there are still some limitations and deficiencies, which are outlined as follows.
1. Connection and Interconnection on the Shop-Floor Previous researchers have mainly focused on theoretical, technical, and methodological studies for a single physical element (e.g., a person, a machine, a product) on the shop-floor, such as works on data collection, data transmission and processing, state monitoring and health management, etc. Commercially available devices have also been developed to implement the related functions. Based on this, the smart operations for a single element can be realized to some extent. However, there has been insufficient attention paid to the interconnection between the heterogeneous physical elements (e.g., machinemachine, manmachine, machineenvironment, humanmachineenvironment) on the shop-floor. Particularly, the connection and interconnection of all the heterogeneous physical elements, including persons, machines, materials, and environment are lacking. As a result, theories and universal devices for full interconnection and fusion of the heterogeneous physical elements on the shop-floor would need to be pursued.
2. Digital/Virtual Shop-Floor Modeling/Simulation Previous researchers mainly focused on the construction and simulation of geometry models or system models for the physical elements on the shopfloor, while models that can depict the actual behaviors, rules, and constraints are relatively rare; as a result, a complete mapping from physical elements to virtual models cannot be achieved. In addition, with respect to the shop-floor simulation, previous research mainly relies on the existing data-driven models, and lacks sufficient explorations into the precise relations among shop-floor behaviors, rules, and constraints, as well as their integration methods. Little attention is paid to multidimension fused models that can describe the shop-floor accurately and thoroughly from various aspects such as geometry, physics, behaviors, and rules.
3. Shop-Floor Data/Information Integration Based on information systems or platforms such as manufacturing execution systems (MES), enterprise resource planning (ERP), and computer-aided process planning (CAPP), the information and data involved in the shop floor can be integrated and shared. Although the integration scope covers the existing data from information systems deployed on the shop-floor and part of the collected real-time data from production, data from the upstream business (e.g., market demand and product design), and downstream business (e.g., product maintenance and disposal) are seldom considered. In addition, simulated data from the virtual models and fused data from both the physical elements and virtual models have attracted little attention. In summary, seamless integration and fusion for data from all of the elements, processes, and businesses on the shop-floor are insufficient.
4. Shop-Floor Optimal Operations and Precision Management The traditional method for optimal operations and precision management is mainly based on the process of problem analysismodel constructionalgorithm designoptimization analysis and control. With Internet of Things (IoT) and big data applied to the shop-floor, researchers around the world have proposed a new idea, which transforms the traditional process to data association miningdynamic evolutionsimulation/predictionintelligent control based on shop-floor big data. Both methods rely on models and data from the shop-floor. However, owing to the lack of multidimension fused models and data, the current shop-floor still has various deficiencies in operations and management, such as poor consistency between the physical and virtual spaces, low intelligence, and low accuracy. Therefore, to solve these problems, it is urgent to develop reliable services and service fusion that are driven by data and models. All the deficiencies mentioned above can be summed up as one scientific problem, that is, cyberphysical fusion on the shop-floor. To solve the problem, digital twin (DT) technology is introduced to the shop-floor and the concept of DTS is proposed. In the DTS, through constructing fully interconnected PS, high-fidelity VS, fully integrated and fused SDTD, and highly precise SSS, it becomes possible to realize the cyberphysical fusion on the shop-floor, thus providing a new solution for enterprises to carry out optimal operations and precision management.
The SDTD that fuses the physical and virtual data not only can reflect the states of the PS and the VS, but also drive their operations. The actual demands during the operation of the DTS, such as energy consumption management, precise control, and predictive maintenance, involve a series of complex and dynamic smart decision-making problems. The complexity and dynamism are due to the dynamic and uncertain environment, uncertain states of physical elements, dynamic changes of production tasks, and diverse objectives and constraints. Services can be used to build an important bridge between the manufacturing physical space and the virtual space, which helps to realize smart interconnections and operations [30,31]. The ultimate goal of the physical elements fusion, models fusion, and data fusion in the DTS is to provide various services required in the production based on the SDTD in combination with the existing information systems (e.g., MES). The services herein include production scheduling, product quality management, collaborative process planning , production process control, equipment health management, and energy efficiency optimization analysis . Accordingly, smart production and management on the shop-floor can be achieved through fusion and collaboration of the services through dynamic service invocation, scheduling, and combination. The above process is defined as the scientific problem of data-driven services fusion (i.e., services fusion). The shopfloor service fusion and application mainly involve the following criteria and technologies.
Cyber physical fusion is a common challenge for advanced manufacturing strategies, and also a key scientific problem that needs to be solved for the deployment of the DTS. At present, the DTS has the following shortcomings in production and operation processes: (1) data collection on the shop-floor is incomplete; (2) virtual/digital models are difficult to establish and simulation analysis lacks systematism; (3) bidirectional dynamic mapping and fusion for the physical and virtual data are insufficient; and (4) dynamic and flexible combinations of services toward specific demands are lacking. Based on research on the concept, composition, operating mechanisms, characteristics, and key technologies of the DTS, this chapter designs a reference framework for the DTS and further proposes the scientific problem, that is, the cyberphysical fusion in the DTS. With respect to the composition of the DTS, cyberphysical fusion is divided into four different perspectives, including the physical elements fusion in PS, models fusion in VS, data fusion in SDTD, and services fusion in SSS. The corresponding criteria and key technologies for these fusions are also discussed. This chapter aims at providing a theoretical and technical reference for researchers to carry out studies on the cyberphysical fusion for the DTS.
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