The Evolvable Assembly Systems research programme brings together a multidisciplinary and multi sector partnership drawing upon skills from across the University of Nottingham, with an established track record in transformative research.
Assembly of final products in sectors such as automotive, aerospace, pharmaceutical and medical industries is a key production process in high labour cost areas such as the UK.
Evolvable Assembly Systems
To respond to current challenges manufacturers need to transform current capital-intensive assembly lines into smart systems that can react to external and internal changes. There is a need for a radically new approach toward development of future assembly systems able to continuously evolve in order to respond to changes in product requirements.
The research delivers a new pattern shift in adaptable and cost effective manufacture that breaks with traditional approaches and is affirmed of the following foundational research challenges:
- Product-Process System Evolution
- Data Analytics; Knowledge Modelling
- Emergence Engineering
- Open Manufacturing
The fundamental ‘collective’ pillars are expected to shed new insights on the evolution of future production platforms. The pillars will forecast a game-changing strategy for industries ability to respond and solve current and future societal challenges linked to retaining and expanding manufacturing operations in the UK.
The principle goal of the research programme is to define and validate the vision and support architecture, theoretical models, methods and algorithms for Evolvable Assembly Systems as a new platform for open, adaptable, context-aware and cost effective production.
The proposed programme is a radical departure from the current philosophy of reconfigurable manufacturing – it will create a framework for autonomous context-aware and adaptable assembly and manufacturing systems that can co-evolve products, processes, business and social environment.
The aim will be supported by the following objectives:
- Define the conceptual framework, reference architecture, and control concept for evolvable manufacturing systems
- Create models and decision making support methods for self-learning, context-awareness and self-adaptation in Evolvable Assembly Systems
- Build the methods and algorithms to support a new generation of automated self-adaptive hybrid process systems
- Prototype selected instances of the reference architecture, integrating the developed models and methodologies
- Generate scenarios and prototype demonstrators for evaluation and validation of the proposed models
Our work assists the national and international commercial private sector, with a particular focus on UK manufacturing companies, through supply chain networks who have the potential to engage with new manufacturing partners. Our close collaboration with the industry stakeholders ensures direct impact across multiple manufacturing sectors.
The wider public prospers from the research by the increased ability of organisations to respond to customer needs and to reduce the product cost through the increased responsiveness of the systems, as well as accelerated development of new products.
The programme has been designed to deliver early industrial pilot demonstrators, some results will have an early impact, for example; Airbus and BAE systems will apply the results in future concepts of assembly lines for airframes and structures of future aircraft.
From a policy perspective the research will provide a national focal point for future research in this exciting topic, through a series of road mapping activities supported by the University of Nottingham Institute for Advanced Manufacturing
de Silva, L., Felli, P., Chaplin, J.C., Logan, B., Sanderson, D. and Ratchev, S., 2017, May. Synthesising industry-standard manufacturing process controllers. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (pp. 1811-1813). International Foundation for Autonomous Agents and Multiagent Systems.
Bakker, O.J., Chaplin, J.C., de Silva, L., Felli, P., Sanderson, D., Logan, B. and Ratchev, S., 2017. Toward process control from formal models of transformable manufacturing systems. Procedia CIRP, 63, pp.521-526.
Sanderson, D., Chaplin, J.C., De Silva, L., Holmes, P. and Ratchev, S., 2016, September. Smart manufacturing and reconfigurable technologies: Towards an integrated environment for evolvable assembly systems. In Foundations and Applications of Self* Systems, IEEE International Workshops on (pp. 263-264). IEEE.
de Silva, L., Felli, P., Chaplin, J.C., Logan, B., Sanderson, D. and Ratchev, S., 2016. Realisability of production recipes. ECAI 2016: 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands-Including Prestigious Applications of Artificial Intelligence (PAIS 2016), 285, 1449-1457
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.
Chaplin, J.C., Bakker, O.J., de Silva, L., Sanderson, D., Kelly, E., Logan, B. and Ratchev, S.M., 2015. Evolvable assembly systems: A distributed architecture for intelligent manufacturing. IFAC-PapersOnLine, 48(3), pp.2065-2070.
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.