Prediction of Hardenability of Gear Steel Using Stepwise Polynomial Regression and Artificial Neural Network

被引：2

作者：

Gao Xiuhua ^{[1
]}

Deng Tianyong ^{[1
]}

Wang Haoran ^{[1
]}

Qiu Chunlin ^{[1
]}

Qi Kemin ^{[1
]}

Zhou Ping ^{[2
]}

机构：

[1] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang 110819, Peoples R China

[2] Laiwu Iron & Steel Co Ltd, Laiwu 271104, Peoples R China

来源：

MATERIALS AND PRODUCT TECHNOLOGIES | 2010年 / 118-120卷

关键词：

Hardenability; BP; Stepwise polynomial regression; Neural networks;

D O I：

10.4028/www.scientific.net/AMR.118-120.332

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The prediction of the hardenability of gear steel has been carried using stepwise polynomial regression and artificial neural networks (ANN). The software was programmed to quantitatively predict the hardenability of gear steel by its chemical composition using two calculating models respectively. The prediction results using artificial neural networks have more precise than the stepwise polynomial regression model. The predicted values of the ANN coincide well with the actual data. So an important foundation has been laid for prediction and controlling the production of gear steel.

引用

页码：332 / +

页数：2

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