Prediction of the Cutting Depth of Abrasive Suspension Jet Using a BP Artificial Neural Network

  • Xiaojian Liu
  • Tao Yu
  • Wenbin Wang
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 207)


Abrasive suspension jet is a new embranchment of abrasive jet. In this paper, the abrasive suspension technology is first used in cutting process in domestic market. The abrasive grain concentration in suspension is constant, so the cutting quality is more stable. In this paper, a prediction model based on a back-propagation (BP) artificial neural network is presented for predicting the cutting depth generated by abrasive suspension jet. In the application of the BP neural network, the mean error of the output in the model training is 0.01, the relatively discrepancy is below 8.70%. The modeling method based on the BP neural network is much more convenient and exact compared with traditional methods, and can always achieve a much better prediction effect. It is verified with experiments to be reasonable and feasible, and it is the better foundation for the future study of abrasive suspension jet.

Key words

neural network abrasive suspension jet cutting depth 

6. References

  1. 1.
    Xiao-jian Liu Tao Yu, Study on the Theory of A New Kind of Abrasive Jet Cutting Technology and the Test Analyses, Machinery, 2005.2.Google Scholar
  2. 2.
    Liu Xiaojian, Yu Tao, Liu Linsheng Wang Wenbin “The study on abrasive suspension jet cutting technology”, Lubrication Engineering, 2005,169(3), 31–33Google Scholar
  3. 3.
    Cybenko, G., Mathematics of Control Signal and Systems, 2003, 2, 303.MathSciNetGoogle Scholar
  4. 4.
    J.M. Cabrera, A. Alomar, J.J. Jonas, J.M. Prado, Metall. Trans. A 28 (1997) 2233–2244.Google Scholar
  5. 5.
    Taylor. J.G. (1998) Neural Networks:an overview in L.J. Landau, & J.F. Taylor. Concepts for neural networks — a Survey London: Springer.Google Scholar
  6. 7.
    G. A. Carpenter and S. Huang, The ART of adaptive pattern recognition by self-organizing neural networks, Computer 2004, 22(6),77–88.Google Scholar
  7. 8.
    L. Monostori and D. Barschdorff, Artificial neural networks in intelligent manufacturing, Robotics and Computer Integrated Manufacturing 2002,9(2),412–436.Google Scholar
  8. 9.
    Refenes A. N. Stock performance modeling using neural networks: A Comparative study with regression models, Neural Networks, 1994,17(2).36–48.Google Scholar

Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Xiaojian Liu
    • 1
    • 2
  • Tao Yu
    • 1
  • Wenbin Wang
    • 1
  1. 1.CIMS &Robot Center of Shanghai UniversityShanghaiChina
  2. 2.Shandong Institute of Light Industry ShandongChina

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