Quality prediction of the continuous casting bloom based on the extreme learning machine

CHEN Heng-zhi; YANG Jian-ping; LU Xin-chun; YU Xiang-zhuo; LIU Qing

doi:10.13374/j.issn2095-9389.2018.07.007

Volume 40 Issue 7

Jul. 2018

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Article Navigation > Chinese Journal of Engineering > 2018 > 40(7): 815-821

CHEN Heng-zhi, YANG Jian-ping, LU Xin-chun, YU Xiang-zhuo, LIU Qing. Quality prediction of the continuous casting bloom based on the extreme learning machine[J]. Chinese Journal of Engineering, 2018, 40(7): 815-821. doi: 10.13374/j.issn2095-9389.2018.07.007

Citation:

CHEN Heng-zhi, YANG Jian-ping, LU Xin-chun, YU Xiang-zhuo, LIU Qing. Quality prediction of the continuous casting bloom based on the extreme learning machine[J]. Chinese Journal of Engineering, 2018, 40(7): 815-821. doi: 10.13374/j.issn2095-9389.2018.07.007

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Quality prediction of the continuous casting bloom based on the extreme learning machine

doi: 10.13374/j.issn2095-9389.2018.07.007

1) State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
2) Fangda Special Steel Technology Co., Ltd, Nanchang 330000, China

Received Date: 2017-06-12

Abstract

Abstract

To solve the problems of slow training, weak generalization ability, and low prediction accuracy in the traditional prediction model established in terms of the BP neural network, a method of the quality prediction of the continuous casting bloom based on the extreme learning machine (ELM) was proposed to predict the degree of the center porosity and the central segregation of 60Si2Mn continuous casting bloom produced by Fangda Special Steel. Comparing the prediction models of the BP neural network and the GA-BP neural network, the results show that the prediction accuracy of the model based on ELM is improved to 85% and 82.5% in the center loose and central segregation, respectively, and the operation time is reduced to 0.1 s. The model can rapidly and accurately analyze the quality of a continuous casting billet, thus providing a new method for the online application of continuous casting billet quality prediction.
- continuous casting bloom,
- BP neural network,
- genetic algorithm,
- extreme learning machine,
- quality prediction

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References(11)

References

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