Tool Condition Monitoring Based on an Adaptive Neurofuzzy Architecture

Abstract:

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Metal cutting operations constitute a large percentage of the manufacturing activity. One of the most important objectives of metal cutting research is to develop techniques that enable optimal utilization of machine tools, improved production efficiency, high machining accuracy and reduced machine downtime and tooling costs. Machining process condition monitoring is certainly the important monitoring requirement of unintended machining operations. A multi-purpose intelligent tool condition monitoring technique for metal cutting process will be introduced in this paper. The knowledge based intelligent pattern recognition algorithm is mainly composed of a fuzzy feature filter and algebraic neurofuzzy networks. It can carry out the fusion of multi-sensor information to enable the proposed intelligent architecture to recognize the tool condition successfully.

Info:

Periodical:

Materials Science Forum (Volumes 471-472)

Edited by:

Xing Ai, Jianfeng Li and Chuanzhen Huang

Pages:

196-200

Citation:

P. Fu et al., "Tool Condition Monitoring Based on an Adaptive Neurofuzzy Architecture", Materials Science Forum, Vols. 471-472, pp. 196-200, 2004

Online since:

December 2004

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Price:

$38.00

[1] S.C. Lin and R.J. Yang: Int. J. Machine Tools and Manufacturing Vol. 35-9 (1995), p.1203.

[2] M.A. Elbestawi, T.A. Papazafiriou and R.X. Du: Int. J. Machine Tools Manufacturing Vol. 31-1(1991), p.66.

[3] D.E. Dimla and P.M. Lister: Int. J. of Machine Tools and Manufacturing Vol. 40-5 (2000), p.745.

[4] D.E. Dimla and P.M. Lister: Int. J. of Machine Tools and Manufacturing Vol. 40- 5(2000), p.772.

[5] P. Wilkinson, R.L. Reuben and J.D.C. Jones: Mechanical Systems and Signal Processing Vol. 13-6(1999), p.960.

[6] X. Li, S. Dong and P.K. Venuvinod: Int. J. of Advanced Manufacturing Technology Vol. 16-5(2000), p.305.

[7] X.L. Li, K.S. Tso and J. Wang: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews Vol. 30(2000), p.355.

[8] C. Chungchoo and D. Saini: Int. J. of Machine Tools and Manufacture Vol. 42, No. 1(2002), p.32.

[9] B. Sick: Measurement and Control Vol. 34- 7(2001), p.210.

[10] H.M. Ertunc and K.A. Loparo: Int. J. of Machine Tools and Manufacture Vol. 41-9(2001), p.1352.

[11] J.C. Bezdek: Pattern Recognition with Fuzzy Objective Function Algorithm (Plenum Press, USA, 1981).

[12] M. Brown and C.J. Harris: Int. J. Neural Systems Vol. 6-2(1995), p.212.

[13] L.X. Wang: Adaptive fuzzy systems and control: design and stability analysis (Prentice Hall, Englewood Cliffs, USA. 1994).

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