Abstract
In this paper, we introduce a human–robot collaboration (HRC) mold assembly cell to cope with small-volume mold production and reduce the risk of musculoskeletal disorders (MSDs) on a human worker during manual mold assembly operation. Besides, the wide variety of types and weights of the mold components motivated us to design an HRC system that consists of two robots. Therefore, we propose two collaboration modes for HRC systems using two robots and develop a task-allocation model to demonstrate the application of these collaboration modes in the mold assembly. The task-allocation model assigns a task based on the task characteristics and capability of agents in the collaboration cell. First, we decompose the assembly operation into functional actions to analyze the characteristics of tasks. Then, we obtain the agent assignment preference based on task characteristics and capability of agents using the analytic network process. Finally, we apply the genetic algorithm in the final task allocation to minimize assembly time, use of a less capable agent, and ergonomic risk. This paper contributes to expanding the HRC system with two robots in the mold assembly to allow the execution of a greater diversity of tasks and improve the assembly time and MSD risk level for the human worker.
Highlights
Automated manufacturing is shifting towards an intelligent manufacturing concept with the introduction of the Industry 4.0 strategy [1]
The results show that the number of tasks with medium and high risk was reduced in the human-robot collaboration (HRC) mold assembly cell, and the number further reduced in the 1H:2R(F) mold assembly cell
The results show that the implementation of HRC mold assembly cells reduced human utilization
Summary
Automated manufacturing is shifting towards an intelligent manufacturing concept with the introduction of the Industry 4.0 strategy [1]. In other words, manufacturing operations require high flexibility and adaptability of automation systems to deal with the rapid changes in customer demand, products, and processes [2]. Traditional automated systems using industrial robots are inflexible to adapt to a dynamic manufacturing environment. Traditional automated systems have high performance in mass production, they are not suitable for implementation in low-volume production. Frequent changes in demand increase the cost and technical limitations owing to changes in the layout and robot programming for new products and the integration of safety systems. Human-robot collaboration (HRC) systems have become a potential solution for improving production performance from the aspect of quality and flexibility in automated operations
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