Modeling and Control of Mobile Multi-Skilled Robot Operated Flexible Manufacturing Systems
Bhatta, Kshitij, Mechanical and Aerospace Engineering - School of Engineering and Applied Science, University of Virginia
Chang, Qing, EN-Mech & Aero Engr Dept, University of Virginia
With the advent of Industry 4.0, there has been a tremendous leap in the field of smart manufacturing. Furthermore, recent advancements in machine learning and robotics are making human-robot collaboration a reality, ushering in yet another industrial revolution that many are calling Industry 5.0. In this context, the use of mobile collaborative robots (cobots) offers high flexibility for manufacturing systems, which calls for studies from the production system’s perspective on how to effectively integrate robots into such systems. This thesis aims to provide a mathematical framework for a mobile multi-skilled robot-operated Flexible Manufacturing System (FMS).
The thesis focuses on the development of a comprehensive model for an FMS utilizing mobile multi-skilled robots and the establishment of an effective real-time control strategy using performance metrics derived from the model. The impact of incorporating robots into the manufacturing system is examined, and potential improvements in overall performance are explored.
Initially, a mathematical model for the FMS is established, and performance metrics are derived from the model that are used to solve a robot assignment control problem. Maintenance scheduling and tool changes are then incorporated into the model to create a more holistic representation of a manufacturing system. A comparison is made between models that only consider system-level information and those that only consider process-level information, with the integrated model significantly outperforming the others.
In summary, this thesis contributes to the development of appropriate frameworks for analyzing manufacturing systems that incorporate mobile multi-skilled robots and demonstrates the effectiveness of using performance metrics derived from mathematical models for control. The integration of maintenance scheduling and tool changes into the model provides a more comprehensive and realistic representation of the manufacturing system.
MS (Master of Science)
Industry 4.0, Smart Manufacturing, Reinforcement Learning