Wearing prediction of stellite alloys based on opposite degree algorithm

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 t...

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Bibliographic Details
Published in:Rare metals 2015-02, Vol.34 (2), p.125-132
Main Authors: Yue, Xiao-Guang, Zhang, Guang, Wu, Qu, Li, Fei, Chen, Xian-Feng, Ren, Gao-Feng, Li, Mei
Format: Article
Language:English
Subjects:
Algorithms
Alloys
Back propagation
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
Physical Chemistry
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
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Summary: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.
ISSN:1001-0521
1867-7185
DOI:10.1007/s12598-014-0430-0