Cutter tool failure detection in milling operations using
Hilbert-Huang transform

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姓名

柯湯姆(Tomas Kalvoda) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

Cutter tool failure detection in milling operations using Hilbert-Huang transform
(Cutter tool failure detection in milling operations usingHilbert-Huang transform )

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摘要(中)

本篇研究主要探討端铣刀在切削過程因磨損，進而造成銑床轉軸的加速力與端铣刀切削力頻率訊號的改變，藉由分析此頻率的變化去得知端铣刀受損前後的特性。在此研究將使用時頻分析-黃鍔 (Hilbert-Huang Transform, HHT)進行銑床轉軸的加速力以及端铣刀切削力頻率的分析，找出端铣刀受損前後的特性，並再將分析的結果與傅立葉轉換(Fourier Transform, FT)做比較。端铣刀的切削力之量測方法在本篇研究裡，其運用動力計(dynamometer)進行測，
量測結果發現端铣刀在切削過程中因磨損而改變端铣刀尖端之幾何狀，造成磨損前後切削力的改變。而由HHT 以及FT 分析銑床轉軸速力頻率的變化，皆發現端铣刀的磨損(tool fault)並不會對銑床轉軸加速力頻率造成明顯的影響。但若端铣刀只有輕微的受損(tool wear)，則會對銑床轉軸加速力頻率造成明顯的影響，並可用加速力頻率的變化來診斷端铣刀是否有輕微受損之狀況(tool wear)。且藉由分析加速力頻率在x 軸、y 軸以及z 軸所產生的變化，其顯示加速力頻率在x 軸(垂直於進给速度的向量)上的改變最為明顯。
時頻分析-黃鍔法(HHT)優於調和分析的好處在於HHT能分解端铣刀磨損特性，並可辨識這類非穩態及非線性之訊號，但若使用調和分析則無法觀察出端铣刀磨損訊號之真實特性。

摘要(英)

The presented study contains research related to cutter tool fault detection in machining process. Acceleration and cutting force signals were subjected to analysis in order to find characteristic feature for cutter tool fault detection. The new time – series analysis technique Hilbert-Huang Transform (HHT) was applied to find a recognition pattern for successful tool fault or tool wear recognition. The results were compared to Fourier Transform (FT) which has been massively used to monitor cutting processes. The cutting force signal obtained from dynamometer measurements has shown its good characteristic in order to recognize a cutter tool geometric change which is actually a cutter tool fault. The acceleration signal has shown to be insensitive to cutter tool fault detection in both FT and HHT investigations. The sensitivity of both signals has shown to be a good tool for tool wear diagnostic. The sensitivity of different components has shown that axis which is perpendicular to feed rate is the most suitable for cutter tool conditions monitoring. The advantage of the HHT apart from processing of non-stationary and non-linear signal is absence of harmonics which has been very confusing in cutter tool fault or tool wear recognitions.

關鍵字(中)

★ cutter tool fault detection
★ HHT
★ end-mill cutter tool
★ machining

關鍵字(英)

★ cutter tool fault detection
★ end-mill cutter tool
★ machining
★ HHT

論文目次

Abstract ................................................................................................................................................... i
摘要 ....................................................................................................................................................... ii
Content .................................................................................................................................................. iii
List of figures ........................................................................................................................................ iv
Nomenclature ........................................................................................................................................ vi
1. Introduction .................................................................................................................................... 1
2. Motivation ...................................................................................................................................... 3
2.1 Related studies ........................................................................................................................ 3
2.2 Background of milling ............................................................................................................ 7
2.3 Mechanics of metal removal ................................................................................................... 7
2.3.1 Cutting force for end-mill cutter tool .............................................................................. 9
2.3.2 Dynamic cutting force model for end-mill cutter tool .................................................. 12
2.3.3 Milling time-domain simulation with helical teeth ....................................................... 14
2.4 Tool wear, tool fault ............................................................................................................. 17
2.4.1 Tool wear classification .................................................................................................... 18
2.4.2 Tool life ............................................................................................................................ 18
2.4.3 Tool life and machining dynamics ................................................................................ 19
3. Cutter Tool Fault Detection .......................................................................................................... 21
3.1 Simulation of cutting forces .................................................................................................. 21
3.2 Motivation for new technique ............................................................................................... 23
3.3 Cutter tool monitoring using HHT ........................................................................................ 27
3.3.1 Empirical mode decomposition (EMD) ........................................................................ 27
3.3.2 Ensemble empirical mode decomposition .................................................................... 32
3.3.3 EMD and EEMD diagnostic ......................................................................................... 35
3.3.4 Frequency computation ................................................................................................. 36
3.3.5 Marginal spectra ........................................................................................................... 39
3.4 Experimental results – cutting force ..................................................................................... 42
3.4.1 Cutting force data – time domain analysis .................................................................... 45
3.4.2 The cutter tool fault estimation time domain ................................................................ 48
3.5 Cutter tool fault estimation in frequency domain ................................................................. 55
3.6 Cutter tool fault estimation in time-frequency domain ......................................................... 66
3.7 Discussions on cutter tool fault estimation using force signal .............................................. 67
3.8 Acceleration signal analysis as a cutter tool fault indicator .................................................. 71
4. Tool wear recognition ................................................................................................................... 74
5. Conclusions .................................................................................................................................. 81
6. Bibliographies ............................................................................................................................... 84

參考文獻

1. Lan, M. S., Naerheim, Y. In-process Detection of Tool Breakage in Milling, Journal of Engineering for Industry, Vol. 108, 1986, p. 191-198.
2. Dimla D. E., Lister P.M. On-line metal cutting tool condition monitoring I: force and vibration analyse, International Journal of Machine Tools and Manufacture, Vol. 40,1990, p. 739-768.
3. Zhu K., Wong Y.S., Hong G.S. Wavelet analysis of sensor signals for tool condition monitoring,. 49, 2009, International Journal of Machine Tools and Manufacture, 49,
2009, p. 537-553.
4. Milfelner M. An overwiew of data acquistion system for cutting force measuring and optimization in milling, Journal of Material Processing Technology, Vol. 164-165, 2005 p. 1281-1288.
5. Guinea D., Ruiz A., Barrios L.J. Multi-sensor integration - an automatic feature selction and state identification metohodlogy for tool wear estmation, Computers in Industry, Vol. 17, 1991, p. 121-130.
6. Haber R. An Investigation of tool-wear monitoring in high-speed machining, Sensors and Acutators A, Vol. 116, 2004, p. 539-545.
7. Orbey S.E, Monitoring of turning operation via force signals Part 1: recognition of different tool failure forms by spectral analysis, Wear, Vol. 184, 1995, p. 133-143.
8. Vafei S. Vibration monitoring of high speed spindles using spectral analysis techniques,International Journal of Machine Tools and Manufacture, Vol. 42, 2002, p. 1223–1234.
9. Dimla D. E. Sensor signals for tool-wear monitoring in metal cutting - a review of methods. International Journal of Machine Tools and Manufacture, Vol. 40, 2000, p.
1073–1098.
10. Toh C. K. Vibration analysis in high speed rough and finish milling hardened steel, Journal of Sound and Vibration, Vol. 278, 2004, p. 101–115.
11. Yalcin M. E. Identification of common sensory features for the control of CNC milling operations under varying cutting conditions, International Journal of Machine Tools and Manufacture, 2003, p. 897–904.
12. Kwak J. S. et. al. Application of wavelet transform technique to detect tool failure in turning operations, The International Journal of Advanced Manufacturing Technology,
Vol. 28, 2005, p. 1078-1083.
13. Ullah M. M. S., Harib K., H. Simulation of cutting force using non-stationary Gaussian process, Journal of Intelligent Manufacturing, 2009, p. 1-11.
14. Huang N.E. et. al. On Instantenious Frequency, Advances in Adaptive Data Analysis, Vol. 1, N2, 2009, p. 177-229.
15. Huang N.E. et. al. The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis, Proceedings of Royal Society London A, Vol.
454, 1998, p. 903-995.
16. Huang N.E., Shen S. S. P. Hilbert-Huang Transform and its Applications. Singapore: World Scientific, 2005. p. 311. ISBN: 978-981-256-376-7.
17. Schmitz T. L., Smith K. Scott. Machining Dynamics: From frequency response to improved productivity. Springer,2009, pp. 310. ISBN: 978-0-387-09644-5.
18. Altintas Y., Budak E. Analytical Prediction of Stability Lobes in Milling, Annals of the CIRP, Vol. 44, 1995.
19. Cheng K. Machining Dynamics: Fundamentals, Applications and Practices. Middlesex, UK , Springer, 2009, ISBN: 978-1-84628-367-3.
20. Chiou R. Y., Liang S. Y. Chatter stability of a slender cutting tool in turning with tool wear effect. International Journal of Machine Tools and Manufacture, Vol. 4, 38, 1998,p. 315–327.
21. Fofana M. S., Ee K. C., Jawahir I. S. Machining stability in turning operation when cutting with a progressively worn tool inser, Wear, Vol. 255, 7–12, 2003, p. 1395–1403.
22. Madl J. Theory of cutting processes. Prague : The Czech Technical University in Prague,1989.
23. Tlusty G. Manufacturing Equipment and Processes. Prenticer-Hall, 2000.
24. Wu Z., Huang N. E., Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in Adaptive Data Analysis, Vol. 1, 1, 2009, p. 1-41.
25. Huang N. E., Long S.R. and Shen Z. The mechanism for frequency downshift in nonlinear wave evolution. Advancess in Applied Mechanics, 32, p. 59-111.
26. Oppenheim V., Schafer W. S. Discrete-time signal processing. 2nd Edition. New Jersey : Prentice Hall, 1999. ISBN: 0-13-754920-2.
27. Smith J. O. Mathematics of the Discrete Fourier Transform. Standford, 2002.
28. Scheffer C., Girdhar P. Practical machinery vibration analysis and predictive maintence. Elsevier, 2004. ISBN: 0-7506-6275-1.
29. 于德介, 程軍聖, 楊宇編, 機械故障診斷的Hilbert-Huang 變換方法, ISBN: 9787030170538, 2006, pp. 194.
30. Yan R., Gao R. X. Hilbert - Huang Transform – Based Vibration Signal Analysis for Machine Health Monitoring. IEEE transactions on Instrumentation and Measurement,
Vol. 55, No. 6, 2006, p. 2320 – 2329.

指導教授

黃衍任(Yean-ren Hwang)

審核日期

2010-7-16

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