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Rare Metals

, Volume 34, Issue 2, pp 125–132 | Cite as

Wearing prediction of stellite alloys based on opposite degree algorithm

  • Xiao-Guang Yue
  • Guang Zhang
  • Qu Wu
  • Fei Li
  • Xian-Feng Chen
  • Gao-Feng Ren
  • Mei Li
Article

Abstract

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.

Keywords

Opposite degree algorithm Stellite alloys wearing Back propagation neural network Radial basis function neural network 

Notes

Acknowledgments

This study was 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), and the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120143110005).

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Copyright information

© The Nonferrous Metals Society of China and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Xiao-Guang Yue
    • 1
  • Guang Zhang
    • 1
  • Qu Wu
    • 2
  • Fei Li
    • 1
  • Xian-Feng Chen
    • 1
  • Gao-Feng Ren
    • 1
  • Mei Li
    • 1
  1. 1.School of Resources and Environmental EngineeringWuhan University of TechnologyWuhanChina
  2. 2.School of Materials Science and EngineeringShanghai UniversityShanghaiChina

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