Digital Twin Application for Dynamic Speed Regulation in Collaborative Robots

This thesis explores the potentialities deriving from the integration of Digital Twin (DT) technology with collaborative robots (cobots) in manufacturing framework, focusing on the energy consumption optimization, improving operational efficiency and economic profitability. Digital Twin, the virtual representation of physical systems made possible by a continuous exchange of data, allows for real-time data monitoring and collateral adjustment based on a continuous performance evaluation, facilitating the dynamic adaptation of cobots based on real-world conditions. This approach is helpful for manufacturers who increasingly aim to balance productivity with sustainability goals. The study comes from the recent evolution and implementation of the Digital Twin technology in the manufacturing field. This evolution also addresses key challenges for further development, including the lack of standardization and the complexity of integrating real-time data into virtual models. Moving from this point, this research examines the role and the possible interaction of collaborative robots, contrasting them with traditional industrial robots. Unlike conventional robots, cobots are designed to work safely alongside humans, enhancing flexibility and enabling dynamic responses to changes in production demands. From a social point of view, this would allow humans to have a key role in the process, defining a new anthropocentrism, based on a strict relationship between the human and the machine. While taking up a critical vision, this thesis explores potential integration of cobots and Digital Twin technology, assuming the hypothesis of a more efficient energy management in the full process. In fact, mirroring cobots in a virtual environment, real-time adjustments can be made to their operating parameters, optimizing energy consumption based on factors such as payload and changes in dynamic parameters. The study provides a detailed case study conducted at Mind4Lab of Politecnico di Torino, where simulations using the FlexSim software were employed to test different energy optimization strategies, as the dynamic speed regulation. This case study serves as a practical example of how Digital Twin and cobots can work together to reduce energy usage, starting from a strict comparison with the data gathered in a Digital Shadow framework without the intervention of the virtual workspace. Presenting the results of various simulation scenarios, the research wants first to evaluate the energy consumption across different speed settings for cobots, revealing the optimal conditions for balancing speed and energy use, while demonstrating that dynamic speed regulation can significantly enhance energy efficiency, without renouncing to the productivity dimension. Therefore, this work is an opportunity to emphasize the potentiality of integrating Digital Twin with cobots as a pathway to achieve both operational and sustainability objectives in modern manufacturing. By leveraging advanced simulation techniques and real-time data integration, this thesis contributes to the growing body of research focused on sustainable industrial automation, offering a framework for future developments in energy-efficient collaborative robotics.