Digital Twin for Enhancing Human-Robot Collaboration through Dynamic Speed Allocation

Technological advancements surged the demand for customized production, driving manufacturers to move toward mass customization and leading to the emergence of the concept of Industry 5.0. This paradigm emphasizes the social values and sustainability of a production system. In this transition, human-centric manufacturing has become the focus, and issues relating to the collaboration of the operator and the robot shifted from providing only a safe environment for the operator to creating a workplace where the well-being of the human operator is the key. In a collaborative workstation, as also mentioned by other researchers, one critical factor influencing human experience is the speed of the collaborative robot (Cobot). A higher speed of the Cobot can increase the temporal overload experienced by the operator. Therefore, one of the critical challenges is synchronizing the speed of the Cobot and human operator to gain both production efficiency and operator well-being. This thesis study aims to address this issue by proposing a digital twin framework that allows for dynamic speed allocation in response to real-time data. The framework is implemented in Mind4Lab, utilizing FlexSim simulation software, a UR3e collaborative robot, an Eye vision system, and the Modbus communication protocol. It is applied to two case studies: the first scenario operates without the proposed digital twin, while the second case integrates the digital twin. A comparison between the results demonstrated significant improvements in throughput and reduced queue time per item while suggesting a comfortable and effortless environment for the human operator.