Online prediction of diffusion wear on the flank through tool tip temperature in turning using artificial neural networks

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 220, Issue 12, 2006, Pages 2069-2076

  Rao, C H Srinivasa ^a   Rao, D Nageswara ^a   Rao, R N Someswara ^a  

^a ANDHRA UNIVERSITY   (India)

Author keywords

Back propagation neural network; Flank wear; Tool condition monitoring; Tool tip temperature; Volume of diffused material

Indexed keywords

BACKPROPAGATION; CONDITION MONITORING; DIFFUSION; NEURAL NETWORKS; ONLINE SYSTEMS; OPTIMIZATION; THERMOCOUPLES;

BACKPROPAGATION NEURAL NETWORK; FLANK WEAR; TOOL CONDITION MONITORING; TOOL TIP TEMPERATURE; VOLUME OF DIFFUSED MATERIAL;

WEAR OF MATERIALS;

EID: 34247161336     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544054JEM541     Document Type: Article

References (19)

1. Dornfeld, D. A. Acoustic emission process monitoring for untended manufacturing. Proceedings of the Japan USA Symposium on Flexible automation, JAACE, Osaka, Japan, 1986.
2. Shaw, M. C. Metal cutting principles, 1984 (Clarendon Press, Oxford).
3. Rubenstein, C. An analysis of tool life based on flank face wear. ASME J Engng Ind., 1976, 98, 221-226.
4. Opitz, H. and Konig, W. On the wear of cutting tools. Proceedings of 8th Machine Tool Design and Research Conference, Manchester, 1967, pp. 173-190.
5. Koren, Y. Flank wear model of cutting tools using control theory. Trans. ASME J. Engng Ind., 1978, 100, 103-109.
6. Koren, Y. and Lenz, E. Mathematical model for the flank wear while turning steel with carbide cutting tools. Proceedings of CIRP seminar on Manufacturing systems, 1972, Vol. 1, pp. 127-137.
7. Yen, D. W. and Wright, P. K. Adaptive control in machining - A new approach based on the physical constraints of tool wear mechanisms. Trans. ASME J. Engng Ind., 1988, 105, 56-64.
8. Lister, P. M. On-line measurement of tool wear. PhD Thesis, Manufacturing and Machine Tools Division, Department of Mechanical Engineering, UMIST, Manchester, UK, 1993.
9. Dan, L. and Mathew, J. Tool wear and failure monitoring techniques for turning-a review. Int. J. Machine Tools Mf., 1990, 30(4), 579-598.
10. Boothroyd, G. Fundamentals of metal machining and machine tools. 1975 (McGraw-Hill, New York).
11. Trent, E. M. Metal cutting. 1977 (Butterworths, London).
12. Martin, K. F., Brandon, J. A., Grosvenor, B. I., and Owen, A. A comparison of in process tool wear measurement methods in turning. Proceedings of 26th International Machine Tool Design and Research Conference, Manchester, 1986, pp. 289-296.
13. Rangwala, S. Machining process characterization and intelligent tool condition monitoring using acoustic emission signal analysis. PhD Thesis, Mechanical Engineering Department, UC at Berkeley, USA, 1988.
14. Liu, T. I. and Anantharaman, K. S. Intelligent classification and measurement of drill wear. Trans. ASME J. Engng Ind., 1994, 116, 392.
15. Ko, T. J. and Cho, D. W. Cutting state monitoring in milling by a neural network. Int. J. Machine Tools Mf, 1994, 34(5), 659-676.
16. Balazinski, M., Czogala, E., Jemielniak, K., and Leski, J. Tool condition monitoring using artificial intelligence method. Engng Applic. Artif. Intell, 2002, 15, 73-80.
17. Hagan, M. T., Demuth, H. B., and Beale, M. Neural network design, 2002 (Thomson Asia, Singapore).
18. Rao, D. N. Investigations into tool-wear monitoring in turning. PhD Thesis, IIT, Delhi, India, 1991
19. Srinivasa Rao, Ch. On-line assessment of tool wear in turning - diffusion aspects. PhD Thesis, Andhra University, Visakhapatnam, India, 2003.