Wearing prediction of stellite alloys based on opposite degree algorithm  

作　　者：Xiao-Guang Yue Guang Zhang Qu Wu Fei Li Xian-Feng Chen Gao-Feng Ren Mei Li 

机构地区：[1]School of Resources and Environmental Engineering, Wuhan University of Technology [2]School of Materials Science and Engineering, Shanghai University

出　　处：《Rare Metals》2015年第2期125-132,共8页稀有金属（英文版）

基　　金：financially supported by the National Natural Science Foundation of China (Nos. 51374164, 51174153, 51104111 and 51104112);the Self-Determined and Innovative Research Funds of Wuhan University of Technology (No.2014-JL-007);the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120143110005)

摘　　要：In order to predict the wearing of stellite alloys, the related methods of rare metals data processing were discussed. The method of opposite degree （OD） algorithm was put forward to predict the wearing of stellite alloys. OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation （BP） and radial basis function （RBF） neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.In order to predict the wearing of stellite alloys, the related methods of rare metals data processing were discussed. The method of opposite degree （OD） algorithm was put forward to predict the wearing of stellite alloys. OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation （BP） and radial basis function （RBF） neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.

关 键 词：Opposite degree algorithm Stellite alloyswearing Back propagation neural network Radial basisfunction neural network 

分 类 号：TG146.411[一般工业技术—材料科学与工程]