About the Project
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.
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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