## 1 Introduction
They discussed four essential parts: the physical workshop, virtual workshop, workshop service system, and DT data. Furthermore, the data transfer service's operational mechanism and implementation method are studied to lay a theoretical foundation for constructing the DT workshop. However, the research is at an initial stage and still needs a lot of work, such as the applications of a DT workshop in smart manufacturing. [PERSON] [21] et al., proposed a multi-robot system, which can combine robots from different manufacturers. The system is modeled, designed automatically, and monitored in real time through a DT system. Meanwhile, it is well suited for operator training, real-time monitoring, and optimization. However, this method does not involve real-time connection between the twin system and the physical system, and some functions have not been realized. [PERSON] [22] et al., proposed a flexible automatic assembly of CBs by using a robot system. To optimize the assembly trajectory of the manipulator, quintic polynomial interpolation is combined with particle swarm optimization (PSO). However, all experimental verifications are based on a platform for testing, and there is a lack of digital simulation methods for verification. In Ref [23], a framework that supports robot motion planning and motion control based on the DT technique was proposed. Aiming at a flexible assembly robot unit, a mathematical model of robot motion control was then applied to the digital scene of the physical object. The digital model was kept up to date by continuously mirroring the physical units driven by data, thus ensuring the real-time synchronization and faithful mapping of digital twins. However, the research is limited to the design of the robot unit and does not involve the design of the entire flexible manufacturing process of CBs. Moreover, the operation mode was also limited to a single-robot system, which cannot realize flexible automatic production of CBs. In Ref [24], to solve the trajectory planning problem of multiple robots in common welding, a hierarchical planning control strategy with a symmetric internal and external adaptive variable impedance control method is proposed. Although the method can successfully complete the welding of the target, there are certain safety problems during the operation, and the actual operation is difficult. In Refs [25; 26], a deep Q-network (DQN) algorithm in reinforcement learning is applied to the multi-robot system. Compared with the traditional machine-learning algorithm, it can solve the path planning problem faster and more effectively. However, this method has problems, such as low sample utilization and inability to handle continuous actions. The existing research shows that the current CBs manufacturing solutions are mainly semi-automatic, and there is a lack of fully automated and flexible system solutions. Due to the limited capability of a single-robot system, the system beat is affected by the lead time (LT), the actual Takt time (ATT), and the cycle time (CT). In addition, there is a lack of a complete set of digital devices to supervise and optimize the manufacturing system. Compared with existing research, the main innovations and contributions of our study consist of three parts:  
* To realize the flexible assembly of a circuit breaker, a multi-robot collaborative CBFMS is designed in this paper. Compared with traditional assembly methods, it can achieve efficient assembly and enhance system stability. * To improve the assembly efficiency of CBFMS key units and improve the production rhythm of the system, a two-arm cooperation robot method is designed. The training robot is assembled using the depth deterministic strategy gradient (DDPG) algorithm [27; 28; 29]. Compared with other machine-learning algorithms, DDPG can learn continuous actions more effectively, thus improving the stability and convergence of the system. * To monitor and optimize the production unit, a DT system is developed to synchronize the virtual unit by interacting with the physical production unit.