热轧C-Mn钢工业大数据预处理对模型的改进作用

doi:10.13228/j.boyuan.issn0449-749x.20150584

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Abstract It is easy to construct a model that meets a certain requirement of precision through neural network base on big data of C-Mn steels. In this case， the original industry data are usually without preprocessed or preprocessed by remove the abnormal value simply. However， there will comes a situation that is contrary to the objective laws if the regularity of the model is further studied. This is due to a large amount of data in the original data to interfere with each other and the discrete distribution of the industry data. Therefore， in order to construct a reasonable model， redundant and large error data must be removed， while the distribution of train data and prediction data must be uniform. In this way， the amount of calculation of the model is reduced while a significant regularity of data is excavated. For the sake of verify the hypothesis of ways to use big data， Bayes regularization neural network was selected to construct a model for mechanical properties of multi-steel number. At the same time， the process parameters which influence on yield strength were analyzed. By statistics， the prediction accuracies of yield strength and tensile strength data are 96.64% and 99.16%， respectively， of which the absolute error between the predicted value and the measured value lies in the ±30 MPa. Among the predicted data of the elongation rate， 85.71% of the data absolute error between predicted value and measured value is within ±4%.

Key words： big data modeling Bayes neural network C-Mn steel yield strength

Received: 06 December 2015 Published: 26 April 2016

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	WU Sai-Wei
	LIU Zhen-Yu
	ZHOU Xiao-Guang
	SHI Ai-An

Cite this article:

WU Sai-Wei,LIU Zhen-Yu,ZHOU Xiao-Guang等. Hot rolling C-Mn steel industry big data preprocessing for improvements on the model[J]. Iron and Steel, 2016, 51(5): 88-94.

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http://www.chinamet.cn/Jweb_gt/EN/10.13228/j.boyuan.issn0449-749x.20150584 OR http://www.chinamet.cn/Jweb_gt/EN/Y2016/V51/I5/88

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