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Multi-task prediction of hot rolled steel mechanical properties based on CA-MMCE | |
Zhang, Qiwen; Wang, Yichao | |
2024-06 | |
在线发表时间 | 2024-03 |
发表期刊 | Materials Today Communications |
卷号 | 39 |
摘要 | The accurate prediction of mechanical properties of multi-task hot rolling steel is of great significance to the development of manufacturing, construction, and other industries, and can effectively improve the efficiency of continuous quality control of the hot rolling process. Aiming at the problem that the existing deep models lack interpretability in the field of steel rolling, the hot rolling process was deeply analyzed, and the temperature fluctuation was integrated into the input features as a process variable through feature augmentation to obtain the correlation between features. In order to solve the problem that the conventional Attention mechanism only focuses on the overall global information, a Convolutional Attention mechanism (CA) is proposed to focus on local feature information and improve the ability of the network to extract local key features. At the same time, so as to solve the negative transfer problem of the multi-task model, a Multi-gate Mixture of Convolutional Experts (MMCE) is proposed, which uses multi-gate to adjust the influence of each convolutional expert network on different tasks to capture the correlation between tasks. Thus, it can ensure that multiple tasks can be accurately predicted. Experimental results show that, compared with the existing models, the proposed model achieves excellent prediction performance in multi-task prediction and is advanced. © 2024 |
关键词 | Convolution Convolutional neural networks Forecasting Hot rolled steel Hot rolling Learning systems Attention mechanisms Convolutional neural network Hot rolled alloy steel Hot rolled steels Hot rolling process Hot-rolled Mechanical property prediction Multi tasks Multitask learning Rolled alloys |
DOI | 10.1016/j.mtcomm.2024.108594 |
收录类别 | EI ; SCIE |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China [62162040, 62063021] |
WOS研究方向 | Materials Science |
WOS类目 | Materials Science, Multidisciplinary |
WOS记录号 | WOS:001206981500001 |
出版者 | Elsevier Ltd |
EI入藏号 | 20241115746962 |
EI主题词 | Alloy steel |
EI分类号 | 531.1 Metallurgy ; 535.1.2 Rolling Mill Practice ; 545.3 Steel ; 716.1 Information Theory and Signal Processing |
原始文献类型 | Journal article (JA) |
EISSN | 2352-4928 |
引用统计 | 无
|
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/170286 |
专题 | 计算机与通信学院 |
通讯作者 | Wang, Yichao |
作者单位 | College of Computer & Communication, Lanzhou University of Technology, Lanzhou; 730050, China |
第一作者单位 | 兰州理工大学 |
通讯作者单位 | 兰州理工大学 |
第一作者的第一单位 | 兰州理工大学 |
推荐引用方式 GB/T 7714 | Zhang, Qiwen,Wang, Yichao. Multi-task prediction of hot rolled steel mechanical properties based on CA-MMCE[J]. Materials Today Communications,2024,39. |
APA | Zhang, Qiwen,&Wang, Yichao.(2024).Multi-task prediction of hot rolled steel mechanical properties based on CA-MMCE.Materials Today Communications,39. |
MLA | Zhang, Qiwen,et al."Multi-task prediction of hot rolled steel mechanical properties based on CA-MMCE".Materials Today Communications 39(2024). |
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