Assembly strategy modeling and selection for human and robot coordinated cell assembly

Manufacturing industry tends to employ more flexible assembly cells for High-Mix, Low-Volume production. We has proposed an innovative human and robot hybrid assembly cell within this purpose to solve the problem of persistent growing cost for human resources and now and again changes in programs and configurations for robots, and to achieve a high manufacturing efficiency. One of the key issues is to find out the optimal way of allocating the assembly subtasks to both human and robot. In this paper, a model for assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly is proposed. A Dual Generalized Stochastic Petri Net (GSPN) model is theoretically researched and then built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost for possible strategies, and Multiple-Objective Optimization (MOOP) method related Cost-effectiveness analysis is adopted to select the optimal ones. We demonstrate the effectiveness of this approach by comparing the simulation and experimental results.