A Study on Friction Coefficient Prediction of Hydraulic Driving Members by Neural Network

신경회로망에 의한 유압구동 부재의 마찰계수 추정 에 관한 연구

  • 김동호 (문경대학 자동차기계계열)
  • Published : 2003.10.01


Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.


  1. JAST v.39 no.7 Oil Contamination Problem and Oil Cleaning Technology for Contamination Control Sasaki,A.
  2. J. of Wear v.21 A Method for the Study of Wear Particles in Lubricating Oil Sefert,W.W.;Westcott,V.C. https://doi.org/10.1016/0043-1648(72)90247-5
  3. J. of Wear v.142 Computer Image Analysis for Identification of Wear Particles Thomas,H.;Davies,A.D.;Luxmoore,A.R. https://doi.org/10.1016/0043-1648(91)90165-Q
  4. J. of Wear v.142 The Use of Automated Image Analysis for the Study of Wear Particles in Oil-Lubricated Tribological Systems Uedelhoven,W.;Franzl,M.;Guttenberger,J. https://doi.org/10.1016/0043-1648(91)90155-N
  5. J. of Wear v.179 EHL and the Use of Image Analysis Hoglund,E. https://doi.org/10.1016/0043-1648(94)90218-6
  6. Trans. of KSMTE v.7 no.6 Decision for Moving Condition of the Machine Driving System by Artificial Neural Network Park,H.S.;Seo,Y.B.;Lee.C.Y.;Cho,Y.S.
  7. Neural Networks Thoeory an Application Kim,D.S.
  8. Trans. of KSMTE v.7 no.3 Development of In-process Condition Monitoring System on Turning Process Using Artificial Neural Network Lee,S.S.