Industry 4.0 (I4.0) combines artificial intelligence, advanced robotics, the Internet of Things (IOT), data-intensive analytics, and simulation to enable manufacturing systems with smarter, decentralized, interoperable, and collaborative capabilities [1]. A digital twin (DT), an important component of I4.0 systems, is a computer representation of an actual system or process. This computer representation provides testing and optimization capabilities of equipment, products, and material handling devices. Digital twins are capable of interacting with other digital twins as they would interact in a real environment. Collaborative Robots (cobots) assist human operators in performing laborious and repetitive tasks during manufacturing assembly operations. In order to create effective collaborations between the human and the robot, the tasks have to be effectively scheduled. Mathematical programs have been developed to split tasks by maximizing productivity and minimizing human strain [2] . However, these programs are based on static analysis of human fatigue, which overlooks energy-expenditure components of the physical activity caused by human motion variability. As preliminary work, this research group has developed digital twins for human operators using a motion capture system and biometrical sensor technology. Data mining algorithms were used to compare repetitive motions using such digital twin framework [3]. Most recently, the research group developed a proof-of-concept physical simulation framework to test a cobot performing basic order picking and manufacturing assembly operations. The cobot comprises a Universal Robot U3 and a Mobile Industrial Robots MiR 100.
The objectives of this research project are to 1) design simulation-based digital twin models of collaborative robots (cobot), and 2) use these digital twin models to determine which tasks can be performed by a cobot or by a human operator in a prescribed manufacturing operation. These objectives are the continuation of this research team’s preliminary work on development of digital models for Industry 4.0 applications.
Dr. Jimenez leads this project.
[1] B. Vogel-Heuser and D. Hess, “Guest Editorial Industry 4.0–Prerequisites and Visions,” IEEE Transactions on Automation Science and Engineering, vol. 13, pp. 1-3, 02/18 2016.
[2] M. Pearce, B. Mutlu, J. A. Shah, and R. G. Radwin, “Optimizing Makespan and Ergonomics in Integrating Collaborative Robots Into Manufacturing Processes,” IEEE Transactions on Automation Science and Engineering, vol. 15, pp. 1772-1784, 2018.
[3] F. Mendez, D. C. Wierschem, and J. Jiménez, “A Motion Capture System Framework for the Study of Human Manufacturing Repetitive Motions,” 2018.