期刊
ISA TRANSACTIONS
卷 102, 期 -, 页码 193-207出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2020.02.024
关键词
Cyber-physical system; Deep learning; Neural networks; Evolutionary algorithm; Intelligent manufacturing
资金
- National Natural Science Foundation of China [61803327, 61703361]
- Natural Science Foundation of Hebei, China [F2016203249, E2018203162]
- Post-Doctoral Research Projects of Hebei, China [B2019003021]
- Doctoral Foundation of Yanshan University, China [BL18048]
High-speed cold tandem rolling process control system consists of complex mechanical and electrical equipments. The coupling association of these equipments makes multi-objective rolling process complicated to be predicted and controlled. In order to achieve higher prediction precision, a multi-parameter depth perception model is established based on a deep belief network. To get higher control precision in real time, a multi-objective rolling optimization method is introduced, which is supported by many-objective evolutionary algorithm. Five objectives are selected as rolling schedule optimization objective: equal relative power margin, slippage prevent, good flatness, total energy consumption and energy consumption per ton. Simulation results show that many-objective evolutionary algorithm based on decomposition and Gaussian mixture model achieves a set of balance solutions on these objectives. The proposed method could not only predict rolling force and rolling power in real time, but also give the solutions for many-objective reduction schedule. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
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