DBO-DELM Method for Predicting Rolling Forces in Cold Rolling
Aiming at the problems of many assumptions, large computational errors and poor generalisation performance of the traditional rolling force prediction model, a cold rolling force prediction model (DBO-DELM) using the dung beetle optimizer algorithm (DBO) to optimise the deep extreme learning machine...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2024-12-01
|
| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2388 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849416676637933568 |
|---|---|
| author | LI Xiaoyang PIAO Chunhui WANG Xuelei ZHANG Mingzhi |
| author_facet | LI Xiaoyang PIAO Chunhui WANG Xuelei ZHANG Mingzhi |
| author_sort | LI Xiaoyang |
| collection | DOAJ |
| description | Aiming at the problems of many assumptions, large computational errors and poor generalisation performance of the traditional rolling force prediction model, a cold rolling force prediction model (DBO-DELM) using the dung beetle optimizer algorithm (DBO) to optimise the deep extreme learning machine (DELM) is proposed. Based on the Bland-Ford-Hill rolling force model, the characteristic parameters of the DELM rolling force prediction model are selected for each frame of cold continuous rolling. Using the actual production data of a four- frame cold continuous rolling unit in China, the DBO-DELM rolling force prediction model for each frame is generated based on the optimisation of the bias and weight parameters of DELM by the dung beetle optimizer algorithm. The test results show that the relative error of the DBO-DELM rolling force prediction model can reach more than 77% within ± 5% , which is more than 6% higher than that of the existing MA-SVM, DBN, ELM-AE models in the second and third frame, and more than 10% higher than that of the first and fourth frame. In summary, the DBO-DELM rolling force prediction model has obvious improvement in prediction accuracy compared with the existing neural network model in multiple frames and shows good generalisation ability, which provides an effective method for the rolling force prediction in cold continuous rolling. |
| format | Article |
| id | doaj-art-d12bbc84d37a411388c2c095ccb63d82 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-d12bbc84d37a411388c2c095ccb63d822025-08-20T03:33:07ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832024-12-01290611212310.15938/j.jhust.2024.06.011DBO-DELM Method for Predicting Rolling Forces in Cold RollingLI Xiaoyang0PIAO Chunhui1WANG Xuelei2ZHANG Mingzhi3School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043 , China;Hebei Key Laboratory for Electromagnetic Environmental Effects and Information Processing, Shijiazhuang 050043 , ChinaSchool of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043 , China;Hebei Key Laboratory for Electromagnetic Environmental Effects and Information Processing, Shijiazhuang 050043 , ChinaShijiazhuang Yangwang Electromechanical Technology Co. , Ltd. , Shijiazhuang 051432 , ChinaBeijing National Railway Research & Design Institute of Signal & Communication Co. , Ltd. , Beijing 100070 , China;Hebei Key Laboratory for Electromagnetic Environmental Effects and Information Processing, Shijiazhuang 050043 , ChinaAiming at the problems of many assumptions, large computational errors and poor generalisation performance of the traditional rolling force prediction model, a cold rolling force prediction model (DBO-DELM) using the dung beetle optimizer algorithm (DBO) to optimise the deep extreme learning machine (DELM) is proposed. Based on the Bland-Ford-Hill rolling force model, the characteristic parameters of the DELM rolling force prediction model are selected for each frame of cold continuous rolling. Using the actual production data of a four- frame cold continuous rolling unit in China, the DBO-DELM rolling force prediction model for each frame is generated based on the optimisation of the bias and weight parameters of DELM by the dung beetle optimizer algorithm. The test results show that the relative error of the DBO-DELM rolling force prediction model can reach more than 77% within ± 5% , which is more than 6% higher than that of the existing MA-SVM, DBN, ELM-AE models in the second and third frame, and more than 10% higher than that of the first and fourth frame. In summary, the DBO-DELM rolling force prediction model has obvious improvement in prediction accuracy compared with the existing neural network model in multiple frames and shows good generalisation ability, which provides an effective method for the rolling force prediction in cold continuous rolling.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2388cold rollingdata-driven modellingdung beetle optimizerdeep extreme learning machinerolling force prediction |
| spellingShingle | LI Xiaoyang PIAO Chunhui WANG Xuelei ZHANG Mingzhi DBO-DELM Method for Predicting Rolling Forces in Cold Rolling Journal of Harbin University of Science and Technology cold rolling data-driven modelling dung beetle optimizer deep extreme learning machine rolling force prediction |
| title | DBO-DELM Method for Predicting Rolling Forces in Cold Rolling |
| title_full | DBO-DELM Method for Predicting Rolling Forces in Cold Rolling |
| title_fullStr | DBO-DELM Method for Predicting Rolling Forces in Cold Rolling |
| title_full_unstemmed | DBO-DELM Method for Predicting Rolling Forces in Cold Rolling |
| title_short | DBO-DELM Method for Predicting Rolling Forces in Cold Rolling |
| title_sort | dbo delm method for predicting rolling forces in cold rolling |
| topic | cold rolling data-driven modelling dung beetle optimizer deep extreme learning machine rolling force prediction |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2388 |
| work_keys_str_mv | AT lixiaoyang dbodelmmethodforpredictingrollingforcesincoldrolling AT piaochunhui dbodelmmethodforpredictingrollingforcesincoldrolling AT wangxuelei dbodelmmethodforpredictingrollingforcesincoldrolling AT zhangmingzhi dbodelmmethodforpredictingrollingforcesincoldrolling |