Multi-category micro-milling tool wear monitoring with continuous hidden Markov models

Mechanical Systems and Signal Processing

Volumn 23, Issue 2, 2009, Pages 547-560

  Zhu, Kunpeng ^a   Wong, Yoke San ^a   Hong, Geok Soon ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Feature selection; Hidden Markov models; Micro milling; Tool wear monitoring

Indexed keywords

COMPUTATIONAL GRAMMARS; CONDITION MONITORING; COPPER; LEARNING SYSTEMS; MACHINE TOOLS; MACHINING; MARKOV PROCESSES; MICROMACHINING; MILLING (MACHINING); MILLING MACHINES; MOBILE TELECOMMUNICATION SYSTEMS; OBJECT RECOGNITION; PROCESS ENGINEERING; PROCESS MONITORING; STATE ESTIMATION; THEOREM PROVING; TOOL STEEL; WEAR OF MATERIALS;

CASE STUDIES; CLASSIFICATION APPROACHES; CONTINUOUS HIDDEN MARKOV MODELS; CONVENTIONAL MACHINING; CUTTING FORCES; FEATURE SELECTION; HIGH NOISE LEVELS; HIGH PRECISIONS; HMM STRUCTURES; MICRO MILLINGS; MICRO-MILLING; PURE COPPERS; TOOL CONDITION MONITORING; TOOL CONDITIONS; TOOL FLANK WEARS; TOOL WEAR MONITORING; TOOL WEARS;

HIDDEN MARKOV MODELS;

EID: 56249121170     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2008.04.010     Document Type: Article

References (45)

1. Kussul E.M., Raachkovskij D.A., Baidyk T.N., and Talayev S.A. Micromechanical engineering: A basis for the low-cost manufacturing of mechanical microdevices using micro equipment. Journal of Micromechanics and Microengineering 6 (1996) 410-425
2. Liu X., Devor R.E., Kapoor S.G., and Ehmann K.F. The mechanics of machining at the micro-scale: Assessment of the current state of the science. Journal of Manufacturing Science and Engineering 126 4 (2004) 666-677
3. Rahman R., Kumar A.S., and Prakash J.R.R. Micro-milling of pure copper. International Journal of Materials Processing 16 (2001) 39-43
4. Weule H., Huntrup V., and Tritschle H. Micro-cutting of steel to meet new requirements in miniaturization. CIRP Annals 50 (2001) 61-64
5. Friedrich C.R., and Coane P.J. Micromilling development and applications for microfabrication. Journal of Microelectronic Engineering 35 (1997) 367-372
6. In: King R.I. (Ed). Handbook of High-Speed Machining Technology (1985), Chapman & Hall, New York
7. Ikawa N., Shimada S., and Tanaka H. Minimum thickness of cut in micro-machining. Nanotechnology 3 (1992) 6-9
8. M.B. Jun, Modeling and analysis of micro-end milling dynamics, Ph.D. Dissertation, University of Illinois Urbana-Champaign, 2005.
9. Tansel I.N., Rodriguez O., Trujillo M., Paz E., and Li W. Micro-end-milling-I. Wear and breakage. International Journal of Machine Tools & Manufacture 38 (1998) 1419-1436
10. Tansel I.N., Arkan T.T., Bao W.Y., Mahendrakar N., Shisler B., Smith D., and McCool M. Tool wear estimation in micro-machining. Part I: Tool usage-cutting force relationship. International Journal of Machine Tools and Manufacture 40 (2000) 599-608
11. Zhu K.P., Hong G.S., and Wong Y.S. Cutting force denoising in micro-milling tool condition monitoring. International Journal of Production Research (2007) 1-18 doi:10.1080/00207540500469610
12. International Standard Organization (ISO 8688-2: 1989) Tool life testing in milling-Part 2: End milling, 1989.
13. Byrne G., Dornfeld D., Inasaki I., Konig W., and Teti R. Tool condition monitoring (TCM)-the status of research and industrial application. Annals of CIRP 44 2 (1995) 541-567
14. Wu Y., Escande P., and Du R. A new method for real-time tool condition monitoring in transfer machining stations. ASME Journal of Manufacturing Science and Engineering 123 (2001) 339-347
15. Hong G.S., Rahman M., and Zhou Q. Using neural network for tool condition monitoring based on wavelet decomposition. International Journal of Machine Tools and Manufacture 36 5 (1996) 551-566
16. Saglam H., and Unuvar A. Tool condition monitoring in milling on cutting forces by a neural network. International Journal of Production Research 41 7 (2003) 1519-1532
17. Niu Y.M., Wong Y.S., and Hong G.S. Multi-category classification of tool conditions using wavelet packets and ART2 network. ASME Journal of Manufacturing Science and Technology 120 (1998) 807-815
18. Sun J., Hong G.S., Rahman M., and Wong Y.S. Identification of feature set for effective tool condition monitoring by acoustic emission sensing. International Journal of Production Research 42 5 (2004) 901-918
19. Wang W.H., Hong G.S., Wong Y.S., and Zhu K.P. Sensor fusion for on-line tool condition monitoring in milling. International Journal of Production Research 45 21 (2007) 5095-5116
20. Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE 77 2 (1989) 257-286
21. Bunks C., Mccarthy D., and Al-Ani T. Condition-based maintenance of machines using hidden Markov models. Mechanical Systems and Signal Processing 14 4 (2000) 597-612
22. Lee J.M., Kim S.J., Hwang Y., and Song C.S. Diagnosis of mechanical fault signals using continuous hidden Markov model. Journal of Sound and Vibration 276 (2004) 1065-1080
23. Ge M., Du R., and Xu Y. Hidden Markov model based fault diagnosis for stamping processes. Mechanical Systems and Signal Processing 18 (2004) 391-408
24. Li Z., Wu Z., He Y., and Chu F. Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery. Mechanical Systems and Signal Processing 19 2 (2005) 329-339
25. L. P. Heck, J.H. McClellan, Mechanical system monitoring using HMMs, IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, 1991, pp. 1697-1700.
26. Errtunc H.M., Looparo K.A., and Ocak H. Tool wear condition monitoring in drilling operations using Hidden Marko modes. International Journal of Machine Tools and Manufacture 41 (2001) 1363-1384
27. Baruah P., and Chinnam R.B. HMMs for diagnostics and prognostics in machining processes. International Journal of Production Research 43 6 (2005) 1275-1293
28. Wang L., Mehrabi M.G., and Elijah K.A. Hidden Markov model-based tool wear monitoring in turning. ASME Journal of Manufacturing Science and Engineering 124 (2002) 652-658
29. Scheffer C., Engelbrecht H., and Heyns P.S. A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear. Neural Computation and Applications 14 (2005) 325-336
30. Owsley L.M.D., Atlas L.E., and Bernard G.D. Self-organizing feature maps and hidden Markov models for machine tool monitoring. IEEE Transactions on Signal Processing 45 11 (1997) 2787-2798
31. Atlas L., Ostendorf M., and Bernard G. Hidden Markov models for monitoring machining tool-wear. Proceedings of ICASSP (2000) 3887-3890
32. Fish R.K., Ostendorf M., Bernard G.D., and Castanon D.A. Multilevel classification of milling tool wear with confidence estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 1 (2003)
33. O. Cetin, M. Ostendorf, Multi-rate hidden Markov models and their application to machine tool-wear classification, in: The 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), 2004, pp. 837-840.
34. G.V.J. Antonio, A. Juan, Nolazco-Flores, Ruben Morales-Menendez, L. Enrique Sucar, Ciro A. Rodri{dotless}guez, Tool-wear monitoring based on continuous hidden Markov models, in: M. Lazo, A. Sanfeliu (Eds.), CIARP 2005, Lecture Notes in Computer Science, vol. 3773, Springer, Berlin, Heidelberg, 2005, pp. 880-890.
35. Kassim A.A., Zhu M., and Mannan M.A. Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures. Machine Vision and Applications 17 (2006) 327-336
36. Trujillo M., Li W., Fallerio B., Paz E., and Tansel I.N. Inspection of micro-tools at high rotational speeds. International Journal of Machine Tools & Manufacture 34 (1994) 1059-1077
37. Tansel I.N., Rodriguez O., Trujillo M., Paz E., and Li W. Micro-end-milling-III. Wear estimation and tool breakage detection using acoustic emission signals. International Journal of Machine Tools & Manufacture 38 (1998) 1449-1466
38. Bao W.Y., and Tansel I.N. Modeling Micro-end-milling operations. Part III: Influence of Tool Wear. International Journal of Machine Tools & Manufacture 40 (2000) 2193-2211
39. Fraley C., and Raftery A.E. How many clusters? Which clustering method?-Answers via model-based cluster analysis. The Computer Journal 41 (1998) 578-588
40. Altintas Y. Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design (2000), Cambridge University Press
41. Mallat S.G. A Wavelet Tour of Signal Processing (1998), Academic Press, San Diego
42. Wu Y., and Du R. Feature extraction and assessment using wavelet packets for monitoring of machining process. Mechanical Systems and Signal Processing 10 1 (1996) 29-53
43. Pittner S., and Kamarthi S.V. Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 21 1 (1999)
44. K. Goebel, W. Yan, Feature Selection for Tool Wear Diagnosis Using Soft Computing Techniques, GE Technical Information Series, 2001CRD011, February 2001.
45. Theodoridis S., and Koutroumbas K. Pattern Recognition (2003), Academic Press, New York