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


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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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik, “Feature Selection for SVMs”, in Proc. NIPS pp.668-674, 2000,. Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Corinna Cortes, Vladimir Vapnik, “Support-vector networks”, Machine Learning, Vol 20, No. 3, pp. 273-297, September 1995.10.1007/BF00994018 Search in Google Scholar

Nello Cristianini, John Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods”, Cambridge University Press, 2000.10.1017/CBO9780511801389 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

http://www.phmsociety.org/ Search in Google Scholar

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