神经网络在冷轧301S不锈钢极薄带材表面硬度预测中的应用

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Abstract In order to solve the problem of 301S stainless steel strip surface hardness control, the production process of cold rolling 301S stainless steel strip was investigated, and the key process parameters affecting the surface hardness of cold rolling stainless steel strip were obtained, which were tensile strength, rolling speed, temperature of rolling oil and reduction rate. A non linear reflection model was built for predicting the surface hardness of the stainless steel strips by using BP neural network, and the collection of illustrative plates of predicting data were achieved by this model. The results show that the variation of reduction rate affects surface hardness values significantly, and the influence of other parameters on the hardness value is about 10 HV. The relative error between predicted and tested results of the surface hardness is between -2.63% and 2.76%. The neural network model can be used in the field of online control of product quality.

Received: 12 August 2013 Published: 07 May 2014

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	Articles by authors
	GENG Shu-Qing
	LI Zhi-Feng
	MA Yong-Lin

Cite this article:

GENG Shu-Qing,LI Zhi-Feng,MA Yong-Lin. Applications of Neutral Network in Surface Hardness Prediction for Cold Rolling 301S Stainless Steel Thin Strip[J]. Iron and Steel, 2014, 49(5): 63-67.

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http://www.chinamet.cn/Jweb_gt/EN/ OR http://www.chinamet.cn/Jweb_gt/EN/Y2014/V49/I5/63

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