About the Project
The competitiveness of manufacturing companies in modern markets increasingly depends on cost-effective flexible automation technologies. The reconfiguration of a cyber-physical production system, for increasing its capacity or introducing a new product variant, requires extensive human intervention and, therefore, it is time-consuming and expensive. Plug-and-produce systems introduced some levels of autonomy. However, there are currently no known plug-and-produce platforms that can address the full cycle of deployment, control and adaptation of heterogeneous mechatronic components, in particular components from different vendors. Despite achievements to date in the fields of multi-agent systems, plug-and-produce technology and reconfigurable manufacturing systems, fundamental challenges remain. The full potential of the agent-based plug-and-produce approach is still being uncovered, including the ability to handle complex real-time tasks.
This project will support a paradigm shift from a conventional, resource-intensive and largely human-driven configuration and system integration process to plug-and-produce cyber-physical systems with self-awareness and adaptation capabilities. Machine learning and, in particular, deep learning techniques will be used. The key objectives are:
1. To define a vision and software architecture of a multi-agent system framework for plug-and-produce cyber-physical systems.
2. To define a plug-and-produce multi-agent approach for the integration of modules from different equipment suppliers and heterogeneous control systems inside one production line.
3. To develop methods for real-time system awareness using integrated sensor networks, and self-learning capabilities for a robust process optimisation of the entire manufacturing system as well as individual production units.
4. To design advanced distributed control infrastructures and scalable cloud architectures enabling resource virtualisation.
5. To design smart human-machine interfaces that can support proactively the user throughout system evolution.
L. Monostori, B. Kádár, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh, W. Sihn, K. Ueda, “Cyber-physical systems in manufacturing,” CIRP Annals, Volume 65, Issue 2, 2016, Pages 621-641.
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