Dynamic reconfiguration optimization of intelligent manufacturing system with human-robot collaboration based on digital twin

In Industry 4.0, the emergence of new information technology and advanced manufacturing technology (e.g., digital twin, and robot) promotes the flexibility and smartness of manufacturing systems to deal with production task fluctuation. Digital twin-driven manufacturing system with human-robot collaboration is a typical paradigm of intelligent manufacturing. When production task changes, manufacturing system reconfiguration with dynamic opeartion task allocation between operator (human) and robot is a key manner to maintain the production efficiency of intelligent manufacturing system with human-robot collaboration. However, the differences between operator and robot are neglected during reconfiguration of manufacturing system with human-robot collaboration. To promote the reconfiguration accuracy and production efficiency, a dynamic reconfiguration optimization method of intelligent manufacturing system with human-robot collaboration based on digital twin is proposed in this paper, which the different characteristics between operator and robot are considered during reconfiguration optimiztion. Firstly, a multi-objectives optimization model is constructed involving minimum production cost, minimum production time, and minimum idle time to assign operation tasks between operator and robot, where human factor is considered to ensure the production efficiency of operator. Second, nondominated sorting genetic algorithm-II (NSGA-II) is adopted to solve the proposed dynamic reconfiguration optimization model. Finally, a case study is provided to demonstrate the effectiveness of the proposed reconfiguration optimization method for intelligent manufacturing system with human-robot collaboration.