Robust tool wear estimation with radial basis function neural networks

Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME

Volumn 117, Issue 4, 1995, Pages 459-467

  Elanayar, Sunil ^a   Shin, Yung C ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84892166999     PISSN: 00220434     EISSN: 15289028     Source Type: Journal    
DOI: 10.1115/1.2801101     Document Type: Article

References (30)

1. Broomhead, D. S., and Lowe, D., 1988, “Multivariable Functional Interpolation and Adaptive Networks,” Complex Systems, Vol. 2, pp. 321-355.
2. Chryssolouris, G., and Domroese, M., 1988, “Sensor Integration for Tool Wear Estimation in Machining,” Sensors and Controls for Manufacturing, ASME/WAM PED-Vol. 33, pp. 115-123.
3. Cybenko, G., 1989, “Approximations by Superpositions of a Sigmoidal Function,” Mathematical Control Signals Systems, Vol. 2, No. 4, pp. 303-314.
4. Danai, K., and Ulsoy, A. G., 1987, “A Dynamic State Model for On-Line Tool Wear Estimation in Turning,” ASME Journal of Engineering for Industry, Vol. 109, pp. 396-399.
5. Elanayar, S. V. T., and Shin, Y. C„ 1994a, “Radial Basis Function Neural Network for the Approximation and Estimation of Nonlinear Stochastic Dynamic Systems,” IEEE Transactions on Neural Networks, Vol. 5, No. 4, pp. 594-603.
6. Elanayar, S. V. T., and Shin, Y. C., 1994b, “Modeling of Cutting Forces with Worn Tools: Flank Wear Effects,” Materials Issues in Machining II and the Physics of Machining Processes II, TMS and ASME, pp. 341-361, also accepted for publication in Trans. Journal of Engineering for Industry.
7. El Gomayel, J. I., and Bregger, K. D., 1986, “On-Line Tool Wear Sensing for Turning Operations,” ASME Journal of Engineering for Industry, Vol. 108, pp. 44-47.
8. Hastings, W. F., and Oxley, P. L. B., 1976, “Predicting Tool Life from Fundamental Work Material Properties and Cutting Conditions,” Annals of the CIRP, Vol. 25, pp. 33-38.
9. Hingle, T. H., and Pakels, J. H., 1983, “The Practical Application of Diffraction Techniques to Assess Surface Finish of Diamond Turned Parts,” Annals of the CIRP, Vol. 32, No. 1, pp. 499-501.
10. Kannatey-Asibu, Jr. E., and Dornfeld, D. A., 1982, “A Study of Tool Wear Using Statistical Analysis of Metal Cutting Acoustic Emission,” Wear, Vol. 76, No. 2, pp. 247-261.
11. Koren, Y., and Lenz, E., 1972, “Mathematical Models of the Flank Wear while Turning Steel With Carbide Tools,” CIRP Proceedings Manufacturing Systems, Vol. I, No. 2, pp. 12-18.
12. Koren, Y., Ulsoy, A. G., and Danai, K., 1986, “Tool Wear and Breakage Detection Using a Process Model,” Annals of the CIRP, Vol. 35, No. 1, pp. 283-288.
13. Kramer, B. M„ 1986, “On Tool Materials for High Speed Machining,” ASME Journal of Engineering for Industty, Vol. 109, pp. 87-91.
14. Martin, K. F., Brandon, J. A., Grosvenor, R. I., and Owen, A., 1986, “A Comparison of In-Process Tool Wear Measurement Methods in Turning,” Proceedings of the 26th MTDR Conference, pp. 289-296.
15. Martin, P., Mutels, B., and Drapier, J. P., 1974, “Influence of Lathe Tool Wear on the Vibrations Sustained in Cutting,” Proceedings of the 15th MTDR Conference, pp. 251-257.
16. Micheletti, G. F., De Filippi, A., and Ippolito, R., 1969, “Tool Wear and Cutting Forces in Steel Turning,” Annals of the CIRP, Vol. 16, pp. 353-360.
17. Narendra, K. S., and Parthasarthy, K., 1990, “Identification and Control of Dynamical Systems Using Neural Networks,” IEEE Transactions on Neural Networks, Vol. 1, pp. 4-27.
18. Park, J. J., and Ulsoy, A. G., 1989, “On-Line Flank Wear Estimation Using Force Measurement and a Nonlinear Observer,” Control Issues in Manufacturing Processes, ASME/WAM, San Francisco, CA, pp. 55-63.
19. Park, J. J., and Ulsoy, A. G., 1990, “On-Line Flank Wear Estimation Using Adaptive Observers,” ASME/WAM, Dallas, TX, pp. 11-20.
20. Park, J. J., and Ulsoy, A. G., 1993a, “On-Line Flank Wear Estimation Using and Adaptive Observer and Computer Vision, Part I: Theory,” ASME Journal of Engineering for Industry, Vol. 115, No. I, pp. 30-36.
21. Park, J. J., and Ulsoy, A. G., 1993b, “On-Line Flank Wear Estimation Using and Adaptive Observer and Computer Vision, Part II: Results,” ASME Journal of Engineering for Industry, Vol. 115, No. 1, pp. 37-43.
22. Poggio, T., and Girosi, F., 1990, “Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks,” Science, Vol. 24, pp. 978-982.
23. Rangwala, S., and Dornfeld, D. A., 1987, “Integration of Sensors via Neural Networks for Detection of Tool Wear States,” ASME/WAM, pp. 109-120.
24. Rubenstein, C., 1976, “An Analysis of Tool Life Based on Flank Face Wear. Part 1: Theory,” ASME Journal of Engineering for Industty, Vol. 98, No. 1, pp. 221-226.
25. Solaja, V., and Vukelja, D., 1973, “Identification of Tool Wear Rate by Temperature Variation of a Carbide Tip,” Annals of the CIRP, Vol. 22, No. 1, pp. 117-119.
26. Stein, J. L., 1993, “Modeling and State Estimator Design Issues for Model-Based Monitoring Systems,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 115, pp. 318-327.
27. Tonshoff, H. K., Wulfsberg, J. P., Kals, H. J. J., Konig, W., and Van Luttervelt, C. A., 1988, “Developments and Trends in Monitoring and Control of Machining Processes,” Annals of the CIRP, Vol. 37, No. 2, pp. 611-622.
28. Ulsoy, A. G., and Koren, Y., 1993, “Control of Machining Processes,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 115, pp. 301-308.
29. Usui, E., Shirakashi, T., and Kitagawa, T., 1984, “Analytical Prediction of Cutting Tool Wear,” Wear, Vol. 100, pp. 129-151.
30. Wilkinson, A. J., 1977, “Constriction-Resistance Concept Applied to Wear Measurement of Metal Cutting Tools,” Proceedings IEE, Vol. 118, No. 2.