A digital twin-driven flexible scheduling method in a human–machine collaborative workshop based on hierarchical reinforcement learning

Under the influence of the global COVID-19 pandemic, the demand for medical equipment and epidemic prevention materials has increased significantly, but the existing production lines are not flexible and efficient enough to dynamically adapt to market demand. The human–machine collaboration system combines the advantages of humans and machines, and provides feasibility for implementing different manufacturing tasks. With dynamic adjustment of robots and operators in the production line, the flexibility of the human–machine collaborative production line can be further improved. Therefore, a parallel production line is set up as a parallel community, and the digital twin community model of the intelligent workshop is constructed. The fusion and interaction between the production communities enhance the production flexibility of the manufacturing shop. Aiming at the overall production efficiency and load balancing state, a digital twin-driven intra-community process optimization algorithm based on hierarchical reinforcement learning is proposed, and as a key framework to improve the production performance of production communities, which is used to optimize the proportion of human and machine involvement in work. Finally, taking the assembly process of ventilators as an example, it is proved that the intelligent scheduling strategy proposed in this paper shows stronger adjustment ability in response to dynamic demand as well as production line changes.

A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools

Affected by COVID-19, the maintenance process of machine tools is significantly hindered, while unmanned maintenance becomes an emerging trend in such background. So far, three challenges, namely, the dependence on maintenance experts, the dynamic maintenance environments, and unsynchronized interactions between physical and information sides, exist as the main obstacles in its widespread applications. In order to fill this gap, a bio-inspired LIDA cognitive-based Digital Twin architecture is proposed, so as to achieve unmanned maintenance of machine tools through a self-constructed, self-evaluated, and self-optimized manner. A three phases process in the architecture, including the physical phase, virtual phase, and service phase, is further introduced to support the cognitive cycle for unmanned maintenance of machine tools. An illustrative example is depicted in the unmanned fault diagnosis on the rolling bearing of a drilling platform, which validates the feasibility and advantages of the proposed architecture. As an explorative study, it is wished that this work provides useful insights for unmanned maintenance of machine tools in a dynamic production environment.

A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19.

In the wake of COVID-19, the production demand of medical equipment is increasing rapidly. This type of products is mainly assembled by hand or fixed program with complex and flexible structure. However, the low efficiency and adaptability in current assembly mode are unable to meet the assembly requirements. So in this paper, a new framework of human-robot collaborative (HRC) assembly based on digital twin (DT) is proposed. The data management system of proposed framework integrates all kinds of data from digital twin spaces. In order to obtain the HRC strategy and action sequence in dynamic environment, the double deep deterministic policy gradient (D-DDPG) is applied as optimization model in DT. During assembly, the performance model is adopted to evaluate the quality of resilience assembly. The proposed framework is finally validated by an alternator assembly case, which proves that DT-based HRC assembly has a significant effect on improving assembly efficiency and safety.