In-process prediction of corner wear in drilling operations

Journal of Materials Processing Technology

Volumn 101, Issue 1, 2000, Pages 152-158

  Liu, H S ^a   Lee, B Y ^a   Tarng, Y S ^b  

^a NATIONAL FORMOSA UNIVERSITY   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CORRELATION METHODS; DRILLS; POLYNOMIALS; TORQUE; WEAR OF MATERIALS;

CORNER WEAR;

DRILLING EQUIPMENT;

EID: 0033891712     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0924-0136(00)00434-9     Document Type: Article

References (16)

1. Tonshoff H.K., Spintig W., König W., Neises A. Machining of holes developments in drilling technology. Ann. CIRP. 43(2):1994;551-561.
2. Liu T.I., Anantharaman K.S. Intelligent classification and measurement of drill wear. ASME J. Eng. Ind. 116(3):1994;392-397.
3. Noori-Khajavi A., Komanduri R. On multisensor approach to drill wear monitoring. Ann. CIRP. 42(1):1993;71-74.
4. Barton R.P., Thangaraj A.R. A neural network approach to drilled hole quality monitoring based on machine spindle vibrations. Trans. NAMRI/SME. 19:1991;232-239.
5. Lin S.C., Ting C.J. Drill wear monitoring using neural networks. Int. J. Mach. Tools Manuf. 36(4):1996;465-475.
6. König W., Kutzner K., Schehl U. Tool monitoring of small drills with acoustic emission. Int. J. Mach. Tools Manuf. 32(4):1992;487-493.
7. Kashimura Y. Study on detection of drill wear and breakage (first report). Bull. Jpn. Soc. Prec. Eng. 20(3):1986;159-164.
8. Fulcher G.E., Brown D.E. A polynomial network for predicting temperature distributions. IEEE Trans. Neural Networks. 5(3):1994;372-379.
9. R.L. Barron, A.N. Mucciardi, F.J. Cook, J.N. Craig, A.R. Barron, Adaptive learning networks: development and application, in the United States, of algorithms related to GMDH, in: S.J. Farlow (Ed.), Self-Organizing Methods in Modeling: GMDH Type Algorithms, Marcel Dekker, New York, 1984.
10. Chryssolouris G., Guillot M. A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining. ASME J. Eng. Ind. 112:1990;122-131.
11. Montgomery G.J., Drake K.C. Abductive reasoning network. Neurocomputing. 2:1991;97-104.
12. Ravindra H.V., Raghunandan M., Srinivasa Y.G., Krishnamurthy R. Tool wear estimation by group method of data handling in turning. Int. J. Prod. Res. 32:1994;1295-1312.
13. A.R. Barron, Predicted-squared-error: a criterion for automatic model selection, in: S.J. Farlow (Ed.), Self-Organizing Methods in Modeling: GMDH Type Algorithms, Marcel Dekker, New York, 1984.
14. Ivakhnenko A.G. Polynomial theory of complex systems. IEEE Trans. Systems, Man Cybernet. 1(4):1971;364-378.
15. S.J. Farlow (Ed.), The GMDH algorithm, Self-Organizing Methods in Modeling: GMDH Type Algorithms, Marcel Dekker, New York, 1984.
16. Miller G.A. The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63:1956;81-97.