Effective fuzzy-nets training scheme for monitoring tool breakage

Journal of Intelligent Manufacturing

Volumn 11, Issue 1, 2000, Pages 85-101

  Chen, Joseph C ^a  

^a IOWA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; DYNAMOMETERS; FAILURE (MECHANICAL); FUZZY SETS; LEARNING SYSTEMS; MACHINE TOOLS; MILLING (MACHINING);

BACK-UP TRUCK PROBLEM; FUZZY NETS SEARCH SYSTEM; TOOL BREAKAGE DETECTION SYSTEM;

NEURAL NETWORKS;

EID: 0033872261     PISSN: 09565515     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008908309585     Document Type: Article

References (21)

1. Altintas, Y., (1988) In-process detection of tool breakage using time series monitoring of cutting forces. International Journal of Machinery Tool and Manufacture, 28(2), 157-172.
2. Barkman, W. E., (1989) In-Process Quality Control for Manufacturing, Marcek Dekker, Inc., New York.
3. Chen, J. C., (1994) Pokayoke Systems in Unmanned Manufacturing Cells, Ph.D. Dissertation, Auburn University, Alabama.
4. Chen, J. C. and Lin, N., (1997) Optimizing the fuzzy-nets training scheme using the Taguchi parameter design, (in press) International Journal of Advanced Manufacturing Technology.
5. Dickerson, J. A. and Kosko, B., (1993) Fuzzy function learning with covariance ellipsoids. Proceedings IEEE International Conference Neural Networks, 2, 1162-1167, San Francisco.
6. Iwata, K. and Moriwaki, T., (1977) An application of acoustic emission to in-process sensing of tool wear. Annals of the CIRP, 26(1), 21-26.
7. Kong, S. and Kosko, B., (1992) Adaptive fuzzy systems for backing up a truck-and-trailer. IEEE Transactions on Neural Networks, 3(2), 211-223.
8. Kosko, B., (1991) Neural Networks and Fuzzy Systems, Prentice Hall Inc, Englewood Cliffs, New Jersey.
9. Kosko, B., (1992) Fuzzy systems as universal approximators. Proceedings IEEE International Conference Fuzzy System, pp. 1153-1162, San Diego.
10. Lan, M. S. and Naerheim, Y., (1986) In-process detection of tool breakage. Transactions ASME Journal of Engineering Industrial, 108, 191-197.
11. Matsushima, K., Bertok, P. and Sata, T., (1982) In process detection of tool breakage by monitoring spindle motor current of a machine tool. Measurement and Control for Batch Manufacturing, The winter annual meeting of ASME, Phoenix, Arizona, pp. 1419.
12. Tansel, I. N. and McLaughlin, C., (1993) Detection of tool breakage in milling operation - Part 1. The time series analysis approach. International Journal of Machinery Tools Manufacture, 33(4), 531-544.
13. Tansel, I. N. and McLaughlin, C., (1993) Detection of tool breakage in milling operation - Part II. The neural network approach. International Journal of Machinery Tools Manufacture, 33(4), 545-558.
14. Tarng, Y. S., Hseih, Y. W. and Hwang, S. T., (1994) Sensing tool breakage in face milling with a neural network. International Journal of Machinery Tools Manufacture, 34(3), 341-350.
15. Tlusty, J. and Andrews, G. C., (1983) A critical review of sensors for unmanned machining. Annals of the CIRP, 32(2), 563-572.
16. Tlusty, J. and MacNeil, P., (1975) Dynamics of cutting force in end milling. Annals of the CIRP, 24, 21-25.
17. Tong, R. M., (1978) Synthesis of fuzzy models for industrial processes-some recent results. International Journal of General Systems, 4, 143-163.
18. Wang, L. and Mendel, J. M., (1992) Generating fuzzy rules by learning from examples. IEEE Trans. Systems, Management and Cybernetics, 22(6), 1414-1427.
19. Weck, M., Verhaag, E. and Gather, M., (1975) Adaptive control for face-milling operations with strategies for avoiding chatter-vibrations and for automatic cut distribution. Annals of the CIRP, 24-1, 405-409.
20. Yager, R. R. and Filev, D. P., (1994) Essentials of Fuzzy Modeling and Control, John Wiley & Sons, Inc, New York.
21. Zadeh, L. A., (1971) Towards a theory of fuzzy systems, in Aspects of Network and Systems Theory, Kalman, R. and DeClaris, N., Holt, Rinehart & Winston, New York.