Zero-defect manufacturing strategies towards on-line production management for European factories
Funder: European CommissionDuration: October 2016 - September 2020
In this project Brunel will offer event-based adaptive control and optimisation solution to minimise defect in the discrete automated manufacturing process. The machines and the robots will adapt to the variations in material, customer requirements and overall environmental conditions with the purpose to reduce production defect to a minimum and eventually to zero.
People
| Name | Telephone | Office | ||
|---|---|---|---|---|
|
Dr Alireza Mousavi Professor (Principal investigator)
T: +44 (0)1895 265788
E: alireza.mousavi@brunel.ac.uk |
+44 (0)1895 265788 | alireza.mousavi@brunel.ac.uk | Howell Building 231 |
|
Professor Maozhen Li Vice-Dean of the NCUT TNE programme/Professor
T: +44 (0)1895 266748
E: maozhen.li@brunel.ac.uk |
+44 (0)1895 266748 | maozhen.li@brunel.ac.uk | Howell Building 237 |
Outputs
Danishvar, M., Mousavi, A. and Danishvar, S. (2022) 'The Genomics of Industrial Process Through the Qualia of Markovian Behavior'. IEEE Transactions on Systems Man and Cybernetics: Systems, 52 (11). pp. 7173 - 7184. ISSN: 2168-2216 Open Access Link
Danishvar, M., Danishvar, S., Souza, F., Sousa, P. and Mousavi, A. (2021) 'Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model'. Applied Sciences, 11 (4). pp. 1 - 15.Open Access Link
Danishvar, M., Mousavi, A. and Broomhead, P. (2018) 'EventiC: A Real-Time Unbiased Event Based Learning Technique for Complex Systems'. IEEE Transactions on Systems, Man and Cybernetics: Systems, 50 (5). pp. 1649 - 1662. ISSN: 2168-2216 Open Access Link