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  Application of machine learning techniques to the reconfiguration of automated manufacturing systems


   Faculty of Engineering & Digital Technologies

  , ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The competitiveness of manufacturing companies in modern markets increasingly depends on cost-effective flexible automation technologies. Reconfigurable manufacturing systems can accommodate changes to introduce new product variants, adjust production capacity or recover from disruptions. Robots, inspection devices, material handling units and other pieces of equipment can be added to the production system, changed or removed. This level of flexibility requires extensive human intervention and, therefore, it is time-consuming and expensive.

This project aims to investigate the application of machine learning techniques to identify which reconfiguration changes bring the manufacturing system closer to the desired state, in terms of functionality, capacity and efficiency. This will help engineers in future reconfigurations by guiding them throughout the process and will save much time and resources. This research will involve collecting and analysing machine data in order to determine which parameters are appropriate to characterise machine states at various levels, from individual modules to the entire production system. The relationship between the performance of different subsystems and the entire production system will be characterised and conflicting goals will be identified.

It is anticipated that this project will also use multi-agent technology for the distributed control of production systems and semantic models of production systems and products.

How to apply

Formal applications can be made via the University of Bradford web site.

Computer Science (8) Engineering (12)

References

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

D. Scrimieri, R.F. Oates, and S.M. Ratchev, “Learning and reuse of engineering ramp-up strategies for modular assembly systems,” Journal of Intelligent Manufacturing 26 (6): 1063-1076, 2015.

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