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

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.

关键词： Hardenability BP Stepwise polynomial regression Neural networks

作者: GAO Xiuhua DENG Tianyong WANG Haoran QIU Chunlin QI Kemin ZhOU Ping

作者单位: The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China Laiwu Iron and Steel Co.,Ltd. Laiwu 271104, China

会议类型: 国际会议

会议名称: The 2nd International Conference on Advances in Product Development and Reliability(第二届产品开发与可靠性进展国际会议 PDR2010)

会议地点: 沈阳

会议语种:英文

页码: 332-335

在线出版日期: 2010-07-28（万方平台首次上网日期，不代表论文的发表时间）