A comprehensive identification of tool failure and chatter using a parallel multi-ART2 neural network

Journal of Manufacturing Science and Engineering, Transactions of the ASME

Volumn 120, Issue 2, 1998, Pages 433-442

  Li, X Q ^a   Wong, Y S ^a   Nee, A Y C ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

CUTTING; FAILURE (MECHANICAL); FEATURE EXTRACTION; MACHINE TOOLS; NATURAL FREQUENCIES; TURNING;

ACOUSTIC EMISSION SIGNALS; CHATTER; COHERENCE FUNCTION;

NEURAL NETWORKS;

EID: 0032074857     PISSN: 10871357     EISSN: 15288935     Source Type: Journal    
DOI: 10.1115/1.2830144     Document Type: Article

References (32)

1. Bendat, J. S., and Piersol, A. G., 1980, Engineering Applications of Correlation and Spectral Analysis, John Wiley & Sons, Inc.
2. Burke, L. I., 1993, “Unsupervised Neural Network Approach to Tool Wear Identification,” HE Transactions, Vol. 25, No. 1, pp. 16-25.
3. Carpenter, G. A., and Grossberg, S., 1987, “ART2: Self-organization of Stable Category Recognition Codes for Analog Input Patterns,” Applied Optics, Vol. 26, No. 23, pp. 4919-4930.
4. Chryssolouris, G., Domroese, M., and Beaulieu, P., 1992, “Sensor Synthesis for Control of Manufacturing Process,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 114, pp. 158-174.
5. Chung, E. S., Chiou, Y. S., and Liang, S. Y., 1993, “Tool Wear and Chatter Detection in Turning via Time-Series Modeling and Frequency Band Averaging,” Proc. Of the 1993 ASME Winter Annual Meeting, New Orleans, LA, pp. 351-358.
6. Colgan, J., Chin, H., Danai, K., and Hayashi, S. R., 1994, “On-Line Tool Breakage Detection in Turning: A Multi-Sensor Method,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 116, pp. 117-123.
7. Dornfeld, D. A., 1990, “Neural Network Sensor Fusion for Tool Condition Monitoring,” Annals of the CIRP, Vol. 39, No. 1, pp. 101-105.
8. Dong, W., Joe Au, Y. H., and Mardapittas, A., 1992, “Machine Tool Chatter Monitoring by Coherence Analysis,” Int. J. Produc. Res., Vol. 30, No. 8, pp. 1901-1924.
9. Eman, E., and Wu, S. M., 1980, “A Feasibility Study of On-Line Identification of Chatter in Turning Operations,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 102, pp. 315-321.
10. Govekar, E., and Grabec, I., 1994, “Self-Organizing Neural Network Application to Drill Wear Classification,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 116, pp. 233-238.
11. Inasaki, I., and Yonetsu, S., 1981, “In-Process Detection of Cutting Tool Damage by Acoustic Emission Measurement, Proc. 22th MTDR Int. Conf, pp. 261-268.
12. Iwata, K., and Moriwaki, T., 1977, “An Application of AE Measurement to In-process Sensing of Tool Wear,” Annals of the CIRP, Vol. 26, pp. 21-26.
13. Jammu, V. B., and Danai, K., 1993, “Unsupervised Neural Network for Tool Breakage Detection in Turning,” Annals of the CIRP, Vol. 42, No. 1, pp. 67-70.
14. Kannatey-Asibu, E., and Dornfeld, D. A., 1981, “Quantitative Relationships for Acoustic Emission for Orthogonal Metal Cutting,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 103, pp. 330-340.
15. Kannatey-Asibu, E., and Dornfeld, D. A., 1982, “A Study of Tool Wear Using Statistical Analysis of Metal-Cutting Acoustic Emission,” Wear, Vol. 76, pp. 247-261.
16. Koenigsberger, K., and Tlusty, J., 1970, Machine Tool Structures, Pergamon Press, Oxford, UK.
17. Kohonen, T., 1989, Self-Organization and Associative Memory, 3rd Edition, Springer Verlag, Berlin.
18. Lan, M. S., and Dornfeld, D. A., 1984, “In-Process Tool Fracture Detection,” J. Engng Mater. Tech., Vol. 106, pp. 111-118.
19. Li, D., and Mathew, J., 1990, “Tool Wear and Failure Monitoring Techniques for Turning-A Review,” Int. J. Mach. Tools Manufact., Vol. 30, No. 4, pp. 579-598.
20. Li, X. Q., 1991, “Study on the Theory and Application of Automatic Monitoring of Tool Wear and Breakage in FMC,” Ph.D Thesis, Xi’an Jiaotong University, Xi’an, China.
21. Li, X. Q., Jiang, X. F., and Xue, B. Y., 1992, “On-line Diagnosis of Tool Wear and Breakage by AE Spectrum in FMS Based on Neural Network,” Proceedings of 2nd China-CIMS Conference, Shenzhen, China, pp. 177-180.
22. Li, X. Q., Lu, B. H., and Ku, C. H., 1993, “The Recognition of Tool Wear Stage by the Coherence in Cutting Vibration, Its Analytical Model and Experimental Verification,” Chinese Journal of Vibration Engineering, Vol. 6, No. 2, pp. 170-175.
23. Liang, S. Y., and Dornfeld, D. A., 1989, “Tool Wear Detection Using Time Series Analysis of Acoustic Emission,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 111, pp. 199-205.
24. Minis, I. E., Magrab, E. B., and Pandelidis, I. O., 1990, “Improved Methods for The Prediction of Chatter in Turning, Part 1, Determination of Structural Response Parameters,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 112, No. 1, pp. 12-20.
25. Pandit, S. M., and Kashou, S., 1982, “A Data Dependent Systems Strategy of On-Line Tool Wear Sensing,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 104, pp. 217-223.
26. Rangwala, S., and Dornfeld, D. A., 1990, “Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 112, pp. 219-228.
27. Taglia, A. Der., Portunato, S., and Toni, P., 1976, “An Approach to On-Line Measurement of Tool Wear by Spectrum Analysis,” Proc. 17th Int. MTDR Conf., pp. 141-148.
28. Tansel, I. N., Wagiman, A., and Tziranis, A., 1991, “Recognition of Chatter with Neural Networks,” Int. J. Mach. Tools Manufact., Vol. 31, No. 4, pp. 539-552.
29. Tansel, I. N., and Mclaughlin, C, 1993, “Detection ofTool Breakage in Milling Operations-II. The Neural Network Approach,” Int. J. Mach. Tools Manufact., Vol. 33, No. 4, pp. 545-558.
30. Tansel, I. N., 1994, “Identification of The Prefailure Phase in Microdrilling Operations Using Multiple Sensors,” Int. J. Mach. Tools Manufact., Vol. 34, No. 3, pp. 351-364.
31. Tamg, Y. S., Li, T. C, and Chen, M. C, 1994, “On-Line Drilling Chatter Recognition and Avoidance Using An ART2—A Neural Network,” Int. J. Mach. Tools Manufact., Vol. 34, No. 7, pp. 949-957.
32. Tlusty, J., and Andrews, G. C, 1983, “A Critical Review of Sensors for Unmanned Machining,” Annals of the CIRP, Vol. 32, No. 2, pp. 563-572.