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2022 | OriginalPaper | Chapter

9. Tool Wear Monitoring with Hidden Markov Models

Author : Kunpeng Zhu

Published in: Smart Machining Systems

Publisher: Springer International Publishing

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Abstract

In micro-machining, with the miniaturization of the cutting tool (<1 mm in diameter), and high speed (>10,000 rpm) used, the tool wears quickly.

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King RI (ed) (1985) Handbook of high-speed machining technology. Chapman and Hall, New York

Ikawa N, Shimada S, Tanaka H (1992) Minimum thickness of cut in micro-machining. Nanotechnology 3:6–9CrossRef

Vogler MP, De Vor RE, Kapoor SG (2004) On the modeling and analysis of machining performance in micro-end milling, part II: cutting force prediction. J Manuf Sci Eng 126(4):694–704

Liu K (2005) Process modeling of micro-cutting including strain gradient effects. PhD thesis, Woodruff School of Mechanical Engineering, Georgia Institute of Technology

Jun MB (2005) Modeling and analysis of micro-end milling dynamics. PhD dissertation, University of Illinois Urbana-Champaign

Kim J-D, Kim DS (1995) Theoretical analysis of micro-cutting characteristics in ultra-precision machining. J Mater Process Technol 49:387–398CrossRef

Tansel IN, Rodriguez O, Trujillo M, Paz E, Li W (1998) Micro-end-milling—I. wear and breakage. Int J Mach Tools Manuf 38:1419–1436

Tansel IN, Arkan TT, Bao WY, Mahendrakar N, Shisler B, Smith D, McCool M (2000) Tool wear estimation in micro-machining. Part I: tool usage–cutting force relationship. Int J Mach Tools Manuf 40:599–608

Zhu KP, Hong GS, Wong YS (2006) Cutting force denoising in micro-milling tool condition monitoring. Int Jo Prod Res. In Press

10.

International Standard Organization (ISO 8688-2: 1989) Tool life testing in milling-Part 2: end milling

11.

Altintas Y, Yellowley I (1989) The process detection of tool failure in milling using cutting force models. ASME J Eng Ind 111:149–157CrossRef

12.

Byrne G, Dornfeld D, Inasaki I, Konig W, Teti R (1995) Tool condition monitoring (TCM)—the status of research and industrial application. Ann CIRP 44(2):541–567CrossRef

13.

Wu Y, Escande P, Du R (2001) A new method for real-time tool condition monitoring in transfer machining stations. ASME J Manuf Sci Eng 123:339–347

14.

Dimla DE Snr (2000) Sensor signal for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40:1073–1098

15.

Hong GS, Rahman M, Zhou Q (1996) Using neural network for tool condition monitoring based on wavelet decomposition. Int J Mach Tools Manuf 36(5):551–566CrossRef

16.

Saglam H, Unuvar A (2003) Tool condition monitoring in milling on cutting forces by a neural network. Int J Prod Res 41(7):1519–1532CrossRef

17.

Niu YM, Wong YS, Hong GS (1998) Multi-category classification of tool conditions using wavelet packets and ART2 network. ASME J Manuf Sci Technol 120:807–815

18.

Obikawa T, Shinozuka J (2004) Monitoring of flank wear of coated tools in high speed machining with a neural network ART2. Int J Mach Tools Manuf 44:1311–1318CrossRef

19.

Sun J, Hong GS, Rahman M, Wong YS (2004) Identification of feature set for effective tool condition monitoring by acoustic emission sensing. Int J Prod Res 42(5):901–918CrossRef

20.

Wang WH, Hong GS, Wong YS, Zhu KP. Sensor fusion for on-line tool condition monitoring in milling. Int J Prod Res. In Press

21.

Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286CrossRef

22.

Li D, Douglas OS (2003) Speech processing: a dynamic and object-oriented approach. Marcel Dekker, Inc

23.

Bilmes J (2002) What HMMs can do? Technical report, University of Washington

24.

Heck LP, McClellan JH (1991) Mechanical system monitoring using HMMs. In: IEEE international conference on acoustics, speech, and signal processing, vol 3, pp 1697–1700

25.

Errtunc HM, Looparo KA, Ocak H (2001) Tool wear condition monitoring in drilling operations using hidden Marko modes. Int J Mach Tools Manuf 41:1363–1384CrossRef

26.

Baruah P, Chinnam RB (2005) HMMs for diagnostics and prognostics in machining processes. Int J Prod Res 43(6):1275–1293CrossRef

27.

Wang L, Mehrabi MG, Elijah KA (2002) Hidden Markov model-based tool wear monitoring in turning. ASME J Manuf Sci Eng 124:652–658

28.

Scheffer C, Engelbrecht H, Heyns PS (2005) A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear. Neural Comput Appl 14:325–336

29.

Owsley LMD, Atlas LE, Bernard GD (1997) Self-organizing feature maps and Hidden Markov models for machine tool monitoring. IEEE Trans Signal Process 45(11):2787–2798CrossRef

30.

Atlas L, Ostendorf M, Bernard G (2000) Hidden Markov models for monitoring machining tool-wear. In: Proceedings of ICASSP, pp 3887--3890

31.

Randall KF, Ostendorf M, Bernard GD, Castanon DA (2003) Multilevel classification of milling tool wear with confidence estimation. IEEE Trans Pattern Anal Mach Intell 25(1)

32.

Cetin O, Ostendorf M (2004) 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), pp 837–840

33.

Vallejo Jr AG, Nolazco-Flores JA, Morales-Men´endez R, Enrique Sucar L, Rodr´ıguez CA (2005) Tool-wear monitoring based on continuous Hidden Markov models. In: Lazo M, Sanfeliu A (eds) CIARP 2005, LNCS 3773. Springer, Berlin, Heidelberg, pp 880–890

34.

Kassim AA, Mian Z, Mannan MA (2006) Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures. Mach Vis Appl 17:327–336

35.

Konig W, Kutzner K, Schehl U (1992) Tool monitoring of small drills with acoustic emission. Int J Mach Tools Manuf 32:487493

36.

Tansel IN, Mekdeci C, Rodriguez O, Uragun B (1993) Monitoring drill conditions with wavelet based encoding and neural network. Int J Mach Tools Manuf 33(4):559–575CrossRef

37.

Trujillo M, Li W, Fallerio B, Paz E, Tansel IN (1994) Inspection of Micro-tools at high rotational speeds. Int J Mach Tools Manuf 34:1059–1077CrossRef

38.

Tansel IN, Rodriguez O, Trujillo M, Paz E, Li W (1998) Micro-end-milling—III. wear estimation and tool breakage detection using acoustic emission signals. Int J Mach Tools Manuf 38:1449–1466

39.

Bao WY, Tansel IN (2000) Modeling micro-end-milling operations. Part I: analytical cutting force model. Int J Mach Tools Manuf 40:2155–2173

40.

Bao WY, Tansel IN. Modeling micro-end-milling operations. Part III: influence of tool wear. Int J Mach Tools Manuf 40:2193–2211

41.

Tansel IN, Bao WY, Reen NS, Kropas-Hughes CV (2005) Genetic tool monitor (GTM) for micro-end-milling operations. Int J Mach Tools Manuf 45:293–299CrossRef

42.

Zhu KP, Wong YS, Hong GS (2009) Multi-category micro-milling tool wear classification with continuous Hidden Markov models. Mech Syst Signal Process 23:547–560CrossRef

43.

Bouchard G, Celeux G (2006) Selection of generative models in classification. IEEE Trans Pattern Anal Mach Intell 28(4):544–554CrossRef

44.

Theodoridis S, Koutroumbas K (2009) Pattern recognition. Academic Press

45.

Fraley C, Raftery AE (1998) How many clusters? Which clustering method?—answers via model-based cluster analysis. Comput J 41:578–588CrossRef

46.

Athanasopoulou E, Li L, Hadjicostis CN (2009) Maximum likelihood failure diagnosis in finite state machines under unreliable observations. IEEE Trans Autom Control 55(3):579–593

47.

Lee S, Li L, Ni J (2009) Online degradation assessment and adaptive fault detection using modified hidden Markov model. J Manuf Sci Eng Trans ASME 132(2):1–11

48.

Chen C, Zhang B, Vachtsevanos G, Orchard M (2011) Machine condition prediction based on adaptive neuro–fuzzy and high-order particle filtering. IEEE Trans Ind Electron 58(9):4353–4364CrossRef

49.

Zhu JL, Ge Z, Song Z (2015) HMM-driven robust probabilistic principal component analyzer for dynamic process fault classification. IEEE Trans Ind Electron 62(6):3814–3821

50.

Geramifard O, Xu JX, Zhou JH, Li X (2012) A physically segmented hidden Markov model approach for continuous tool condition monitoring: Diagnostics and prognostics. IEEE Trans Ind Inf 8(4):964–973CrossRef

51.

Geramifard O, Xu JX, Zhou JH, Li X (2014) Multimodal hidden Markov model-based approach for tool wear monitoring. IEEE Trans Ind Electron 61(6):2900–2911CrossRef

52.

Nakamura H, Chihara M, Inoki T, Higaki T, Okuda K, Mizuno Y (2009) Impulse testing for detection of insulation failure of motor winding and diagnosis based on Hidden Markov Model, IEEE Trns. Dielectr. Electr. Insul, vol. 17, no.5, pp.1619–1627. Oct. 209.

53.

Ying J, Kirubarajan T, Pattipati KR, Patterson-Hine A (2002) A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests. IEEE Trans Syst Man Cybern Part C Appl Rev 30(4):463–473CrossRef

54.

Camci F, Chinnam RB (2009) Health-state estimation and prognostics in machining processes. IEEE Trans Autom Sci Eng 7(3):581–591

55.

Liu QM, Dong M, Lv WY, Geng XL, Li YP (2015) A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis. Mech Syst Signal Process 64–65:217–232CrossRef

56.

Tobon-Mejia DA, Maderjaher K, Zerhouni N, Tripot G (2012) A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Trans Reliab 61(2):491–503CrossRef

57.

Geramifard O, Xu JX et al (2011) Continuous health condition monitoring: a single hidden semi-Markov model approach. In: Proceedings of IEEE international conference on prognostics and health management (PHM), pp 1–9

58.

Zhu KP, Liu T (2018) On-line high-speed milling tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Trans Ind Inf 14(1):69–78CrossRef

59.

Peng Y, Dong M (2011) A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction. Mech Syst Signal Process 25(1):237–252CrossRef

60.

Wang N, Sun SD, Cai ZQ, Zhang S, Saygin C (2014) A hidden semi-Markov model with duration-dependent state transition probabilities for prognostics. Math Probl Eng 1–10

61.

Yu SZ (2009) Hidden semi-Markov models. Artif Intell 174(2):215–243

62.

Gersho A, Gray RM (2012) Vector quantization and signal compression. Springer

63.

Yu SZ, Kobayashi H (2006) Practical implementation of an efficient forward-backward algorithm for an explicit-duration hidden semi-Markov model. IEEE Trans Signal Process 54(5):1947–1951

64.

Si XS, Wang W, Hu CH, Zhou DH (2011) Remaining useful life estimation—a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14MathSciNetCrossRef

65.

Doucet A, Freitas ND, Gordon N (2001) An introduction to sequential Monte Carlo methods. Sequential Monte Carlo methods in practice. Springer, New York

66.

Mallat SG (1998) A wavelet tour of signal processing. Academic

67.

Jain AK. Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31(8):651–666

Title: Tool Wear Monitoring with Hidden Markov Models
Author: Kunpeng Zhu
Publisher: Springer International Publishing
Book: Smart Machining Systems
Print ISBN: 978-3-030-87877-1

Electronic ISBN: 978-3-030-87878-8

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-87878-8_9

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