Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill
The optimization of rolling schedule is the main content of tandem cold rolling which will affect the quality of products directly. A rolling schedule with the objectives of minimum energy consumption, relative power margin and slippage preventing is established. First, in order to make the rolling...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9079869/ |
| _version_ | 1818624109865598976 |
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| author | Yunlong Wang Jinkuan Wang Chunhui Yin Qiang Zhao |
| author_facet | Yunlong Wang Jinkuan Wang Chunhui Yin Qiang Zhao |
| author_sort | Yunlong Wang |
| collection | DOAJ |
| description | The optimization of rolling schedule is the main content of tandem cold rolling which will affect the quality of products directly. A rolling schedule with the objectives of minimum energy consumption, relative power margin and slippage preventing is established. First, in order to make the rolling schedule more accurate in the calculation process, a mathematical model combines with deep neural network is proposed to calculate the rolling force. Second, a multi-objective particle swarm optimizer with dynamic opposition-based learning is proposed to optimize the rolling schedule. It has a new particle learning strategy to update the moving position of particles. Moreover, opposition-based learning is proposed to make the particles jump out of local optima. Finally, the experiments are carried out based on the field data. Simulation results demonstrate that the accuracy of the rolling force is greatly improved. The proposed algorithm has a promising performance on both diversity and convergence. At the same time, the optimized rolling schedule can well balance the rolling power and prevent slipping between five stands comparing with the original rolling schedule. |
| first_indexed | 2024-12-16T18:51:44Z |
| format | Article |
| id | doaj.art-5bb840a2b83a4f368beaf6217628eab5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:51:44Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5bb840a2b83a4f368beaf6217628eab52022-12-21T22:20:40ZengIEEEIEEE Access2169-35362020-01-018804178042610.1109/ACCESS.2020.29909049079869Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold MillYunlong Wang0https://orcid.org/0000-0001-6238-4781Jinkuan Wang1https://orcid.org/0000-0002-6654-2035Chunhui Yin2https://orcid.org/0000-0002-8497-9740Qiang Zhao3https://orcid.org/0000-0003-2004-1769College of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaSchool of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, ChinaSchool of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, ChinaThe optimization of rolling schedule is the main content of tandem cold rolling which will affect the quality of products directly. A rolling schedule with the objectives of minimum energy consumption, relative power margin and slippage preventing is established. First, in order to make the rolling schedule more accurate in the calculation process, a mathematical model combines with deep neural network is proposed to calculate the rolling force. Second, a multi-objective particle swarm optimizer with dynamic opposition-based learning is proposed to optimize the rolling schedule. It has a new particle learning strategy to update the moving position of particles. Moreover, opposition-based learning is proposed to make the particles jump out of local optima. Finally, the experiments are carried out based on the field data. Simulation results demonstrate that the accuracy of the rolling force is greatly improved. The proposed algorithm has a promising performance on both diversity and convergence. At the same time, the optimized rolling schedule can well balance the rolling power and prevent slipping between five stands comparing with the original rolling schedule.https://ieeexplore.ieee.org/document/9079869/Multi-objective optimizationtandem cold rollingdeep neural networkrolling forcerolling schedule |
| spellingShingle | Yunlong Wang Jinkuan Wang Chunhui Yin Qiang Zhao Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill IEEE Access Multi-objective optimization tandem cold rolling deep neural network rolling force rolling schedule |
| title | Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill |
| title_full | Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill |
| title_fullStr | Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill |
| title_full_unstemmed | Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill |
| title_short | Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill |
| title_sort | multi objective optimization of rolling schedule for five stand tandem cold mill |
| topic | Multi-objective optimization tandem cold rolling deep neural network rolling force rolling schedule |
| url | https://ieeexplore.ieee.org/document/9079869/ |
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