Have a personal or library account? Click to login

Study on Feature Selection and Identification Method of Tool Wear States Based on Svm

By:
Open Access
|Apr 2013

References

  1. Roth John T, Djurdjanovic Dragan, Yang Xiaoping, “Quality and Inspection of Machining Operations: Tool Condition Monitoring”, Journal of Manufacturing Science and Engineering, Vol.132, No.4, pp. 0410151-04101516, 2010.
  2. Teti R, Jemielniak K, O’Donnell G, Dornfeld D, “Advanced Monitoring of Machining Operations”, CIRP Annals—Manufacturing Technology, Vol. 59, No. 2, pp. 717-739, 2010.10.1016/j.cirp.2010.05.010
  3. Abellan-Nebot, Jose Vicente, Romero Subirón Fernando, “A Review of Machining Monitoring Systems Based on Artificial Intelligence Process Models”, The International Journal of Advanced Manufacturing Technology, Vol. 47, No. 1-4, pp. 237-257, 2010.10.1007/s00170-009-2191-8
  4. T.Jayakumar, C.Babu Rao, John Philip, C.K.Mukhopadhyay, J.Jayapandian, C.Pandian, “Sensors for Monitoring Components, Systems and Processes”, International Journal on Smart Sensing and Intelligent Systems, Vol. 3, No. 1, pp. 61-74, March 2010.10.21307/ijssis-2017-379
  5. E. Dimla Snr, “Sensor Signals for Tool-Wear Monitoring in Metal Cutting Operations—A Review of Methods”, International Journal of Machine Tools & Manufacture, Vol. 40, pp. 1073– 1098, 2000.
  6. Boukhenous, S., “A Low Cost Three-Directional Force Sensor”, International Journal on Smart Sensing and Intelligent Systems, Vol. 4, No. 1, pp. 21-34, March 2011.10.21307/ijssis-2017-424
  7. Li Weilin, Fu Pan, Cao Weiqing, “Tool Wear States Recognition Based on Frequency-Band Energy Analysis and Fuzzy Clustering”, In Proceedings of 3rd International Workshop on Advanced Computational Intelligence (IWACI 2010), pp. 162-167, 2010.10.1109/IWACI.2010.5585104
  8. Bernhard Sick, “On-Line and Indirect Tool Wear Monitoring in Turning with Artificial Neural Networks: A Review of More Than A Decade of Research”, Mechanical Systems and Signal Processing, Vol.16, No. 4, pp. 487–546, July 2002.10.1006/mssp.2001.1460
  9. Tony Jebara and Tommi Jaakkola, “Feature Selection and Dualities in Maximum Entropy Discrimination”, Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000), pp. 291-300, 2000.
  10. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik, “Feature Selection for SVMs”, in Proc. NIPS pp.668-674, 2000,.
  11. I. Guyon, J. Weston, S. Barnhill, and N. Vapnik, “Gene selection for Cancer Classification Using Support Vector Machines”, Machine Learning, Vol. 46, no. 1-3, pp. 389–422, 2002.10.1023/A:1012487302797
  12. K-B. Duan, J.C. Rajapakse, and M.F. Nguyen, “One-Versus-One and One-Versus-All MultiClass SVM-RFE for Gene Selection in Cancer Classification”, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Lecture Notes in Computer Science, Vol. 4447, pp. 47-56, 2007.
  13. L.J. Cao, F.E.H. Tay, “Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting”, IEEE Transactions on Neural Networks, Vol. 14, No. 6, pp.1506–1518, 2003.
  14. I. Goethals, K. Pelckmans, J.A.K. Suykens, Bart De Moor, “Subspace identification Of Hammerstein Systems Using Least Squares Support Vector Machines”, IEEE Transactions on Automatic Control, Vol. 50, No.10, pp.1509–1519, 2005.
  15. Corinna Cortes, Vladimir Vapnik, “Support-vector networks”, Machine Learning, Vol 20, No. 3, pp. 273-297, September 1995.10.1007/BF00994018
  16. Nello Cristianini, John Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods”, Cambridge University Press, 2000.10.1017/CBO9780511801389
  17. Guyon, I., Weston, J., Barnhill, S., Vapnik, V. “Gene Selection for Cancer Classification Using Support Vector Machines”, Machine Learning, Vol. 46, No.1-3, pp. 389–422, 2002.10.1023/A:1012487302797
  18. J.A.K. Suykens, J. Vandewalle, “Least Squares Support Vector Machine Classifiers”, Neural Processing Letters, Vol. 9, Vol. 3, pp. 293-300, 1999.10.1023/A:1018628609742
  19. J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, “Least Squares Support Vector Machines”, World Scientific Pub. Co., Singapore, 2002.10.1142/5089
  20. Xavier de Souza, S. Suykens, J. A. K. “Coupled Simulated Annealing”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 40, No. 2, pp. 320–335, 2010.10.1109/TSMCB.2009.202043519651558
  21. Nelder J. A., Mead R., “A Simplex Method for Function Minimization”, Computer Journal, Vol. 7, pp. 308-313, 1965.10.1093/comjnl/7.4.308
  22. http://www.phmsociety.org/
Language: English
Page range: 448 - 465
Submitted on: Jan 24, 2013
Accepted on: Mar 12, 2013
Published on: Apr 10, 2013
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2013 Weilin Li, Pan Fu, Weiqing Cao, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.