Advanced Manufacturing Technology Research Group


There is an increasing pressure on European SMEs to deliver high quality, often customised products using cost effective manufacturing processes and systems while competing in the global market.



One of the key production processes in high labour cost areas such as Europe is assembly of final products in sectors such as automotive, aerospace, pharmaceutical and medical industries. They all require systems that can: be installed quickly, achieve high volumes at shorter time intervals, perform with minimum interruptions and be reconfigured for new products with minimum cost.

PRIME aims to create new solutions for deployment by SMEs of highly adaptive, reconfigurable self-aware plug and produce assembly systems, which will use multi-agent control, dynamic knowledge sharing, integrated monitoring, and innovative human-machine interaction mechanisms. These next generation assembly systems equipped with PRIME technology will be able to proactively support rapid reconfiguration, adaptation, error-recovery, and operational performance improvement. This will lead to a dramatic cost and time reduction of deploying and maintaining complex assembly systems on demand and improve their effectiveness.

The PRIME vision will be achieved by enhancing today’s assembly systems with standardised plug and produce process and control solutions and interfaces to allow rapid reconfiguration and deployment, performance monitoring, self-awareness and evolutionary system adaptation. Based on this, the overall system performance will be monitored against production objectives and bottlenecks, thus errors and sub-optimal behaviour can be identified and assigned to the responsible stations. This will enable optimisation and adaptation of the assembly processes and associated system behaviour within a human centred environment. Furthermore, methods will be developed to integrate existing legacy system modules within the plug-and-produce environment using standardised interfacing.



Scrimieri, D., Antzoulatos, N., Castro, E. and Ratchev, S.M., 2017. Automated experience-based learning for plug and produce assembly systems. International Journal of Production Research, 55(13), pp.3674-3685.

Antzoulatos, N., Castro, E., de Silva, L., Rocha, A.D., Ratchev, S. and Barata, J., 2017. A multi-agent framework for capability-based reconfiguration of industrial assembly systems. International Journal of Production Research, 55(10), pp.2950-2960

de Silva, L., Meneguzzi, F., Sanderson, D., Chaplin, J.C., Bakker, O.J., Antzoulatos, N. and Ratchev, S., 2016. Interfacing Belief-Desire-Intention Agent Systems with Geometric Reasoning for Robotics and Manufacturing. In Service Orientation in Holonic and Multi-Agent Manufacturing (pp. 179-188). Springer, Cham.

Scrimieri, D., Antzoulatos, N., Castro, E. and Ratchev, S.M., 2015. Automated Experience-Based Learning for Plug and Produce Assembly Systems. IFAC-PapersOnLine, 48(3), pp.2083-2088.

Antzoulatos, N., Rocha, A., Castro, E., de Silva, L., Santos, T., Ratchev, S. and Barata, J., 2015. Towards a Capability-based Framework for Reconfiguring Industrial Production Systems. IFAC-PapersOnLine, 48(3), pp.2077-2082.

Sanderson, D., Antzoulatos, N., Chaplin, J.C., Busquets, D., Pitt, J., German, C., Norbury, A., Kelly, E. and Ratchev, S., 2015, September. Advanced manufacturing: An industrial application for collective adaptive systems. In Self-Adaptive and Self-Organizing Systems Workshops (SASOW), 2015 IEEE International Conference on (pp. 61-67). IEEE.

Rocha, A., Di Orio, G., Barata, J., Antzoulatos, N., Castro, E., Scrimieri, D., Ratchev, S. and Ribeiro, L., 2014, July. An agent based framework to support plug and produce. In Industrial Informatics (INDIN), 2014 12th IEEE International Conference on (pp. 504-510). IEEE.

Antzoulatos, N., Castro, E., Scrimieri, D. and Ratchev, S., 2014. A multi-agent architecture for plug and produce on an industrial assembly platform. Production Engineering, 8(6), pp.773-781.

Antzoulatos, N., Castro, E., Scrimieri, D. and Ratchev, S., 2014, February. A multi-agent system architecture for self-configuration. In International Precision Assembly Seminar (pp. 118-125). Springer, Berlin, Heidelberg.

Advanced Manufacturing Technology Research Group

The University of Nottingham
Faculty of Engineering
The University of Nottingham
University Park
Nottingham, NG7 2RD