Application of Neural Network in Corrosion Property of High Speed Steel

摘要：

The article is dedicated to the application of neural network in corrosion property of high Speed Steel. The corrosion properties of high speed steel with about 9wt％ vanadium content and different carbon content were tested under different H3PO4 medium concentration conditions. Using back-propagation (BP) neural network, the non-linear relationship model among the corrosion weight losses (W), corrosion parameters (corrosion time, H3PO4 concentration) and alloy composition (carbon content) is established according to the tested experimental data. The results show that the neural network model can predict the corrosion weight loss precisely according to corrosion conditions and alloy composition. The prediction results reveal that the corrosion resistance of high speed steel decrease with the increase of H3PO4 concentration or carbon content in high speed steel. It is suggested that the corrosion condition and alloy composition should be considered synthetically to estimate the corrosion property of high speed steel.

关键词： Neural network High speed steel Corrosion Carbon content H3PO4

作者: Songmin Zhang Liujie Xu

作者单位: Department of Computer and Information Engineering Luoyang Institute of Science and Technology Luoya Henan Engineering Research Center for Wear of Materials Henan University of Science and Technology L

会议类型: 国际会议

会议名称: 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics 第4届智能人机系统与控制论国际会议 IHMSC 2012

会议地点: 南昌

会议语种:英文

页码: 674-677

在线出版日期: 2012-08-26（万方平台首次上网日期，不代表论文的发表时间）