An intelligent factory prediction model based on big data related analysis and machine learning

Yuelong Zhang; Junjie Diao; Xue Huang; Wanchen Hong; Xiangquan Yin

doi:10.1117/12.2635377

6 May 2022 An intelligent factory prediction model based on big data related analysis and machine learning

Yuelong Zhang, Junjie Diao, Xue Huang, Wanchen Hong, Xiangquan Yin

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 122562P (2022) https://doi.org/10.1117/12.2635377
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

In recent years, with the further development of big data analysis and artificial intelligence technology, many intelligent methods of machine learning have been used in actual production links. The alloy absorption rate refers to the ratio of the weight of the alloying element absorbed by the molten steel during the deoxidation alloying to the total weight of the added element. The main work of this paper is to predict the absorption rate of C and Mn in the iron and steel industry, and realize the automatic optimization and cost control of alloy ingredients in smart factories. However, in the process of molten steel deoxidation and alloying, the alloy absorption rate is affected by many factors, and it is difficult to determine it by an explicit expression. First of all, this paper analyzes the factors that may affect the absorption rate of C and Mn from the perspectives of mechanism and quantification. Secondly, we utilize the canonical correlation analysis method (CCA) to screen out all factors with a correlation coefficient greater than 0.8 as the main influencing factors of element absorption. Finally, comparing the results of the BP and Elman network models, this paper employs the generalized regression neural network (GRNN) to accurately predict the absorption rate.

Citation Download Citation

Yuelong Zhang, Junjie Diao, Xue Huang, Wanchen Hong, and Xiangquan Yin "An intelligent factory prediction model based on big data related analysis and machine learning", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 122562P (6 May 2022); https://doi.org/10.1117/12.2635377

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