Wearing prediction of stellite alloys based on opposite degree algorithm

• Published in: Rare metals Vol. 34; no. 2; pp. 125 - 132
• Main Authors: Yue, Xiao-Guang, Zhang, Guang, Wu, Qu, Li, Fei, Chen, Xian-Feng, Ren, Gao-Feng, Li, Mei
• Format: Journal Article
• Language: English
• Published: Springer Berlin Heidelberg Nonferrous Metals Society of China 01.02.2015; Springer Nature B.V
• Subjects: Algorithms; Alloys; Back propagation; Back propagation neural network; Biomaterials; Chemistry and Materials Science; Cobalt base alloys; Data processing; D算法; Energy; Errors; Materials Engineering; Materials Science; Mathematical models; Metallic Materials; Nanoscale Science and Technology; Neural networks; Opposite degree algorithm; Physical Chemistry; Radial basis function neural network; Stellite alloys wearing; Superalloys; 司太立合金; 基础; 径向基函数（RBF）神经网络; 数据处理; 神经网络方法; 稀有金属; 预测数据; Stellite alloys wearing; Back propagation neural network; Radial basis function neural network; Opposite degree algorithm
• ISSN: 1001-0521; 1867-7185
• DOI: 10.1007/s12598-014-0430-0

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.