Multi-task prediction of hot rolled steel mechanical properties based on CA-MMCE

doi:10.1016/j.mtcomm.2024.108594

IR > 计算机与通信学院

	Multi-task prediction of hot rolled steel mechanical properties based on CA-MMCE
	Zhang, Qiwen; Wang, Yichao
	2024-06
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
语种	英语
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).