Vibration-based estimation of tool major flank wear in a turning process using ARMA models

International Journal of Advanced Manufacturing Technology

Volumn 76, Issue 9-12, 2015, Pages 1631-1642

  Aghdam, B H ^a   Vahdati, M ^a   Sadeghi, M H ^b  

^a K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

^b UNIVERSITY OF TABRIZ   (Iran)

Author keywords

ARMA; Natural modes; Tool wear; Turning; Vibration

Indexed keywords

CUTTING TOOLS; DISPERSIONS; EIGENVALUES AND EIGENFUNCTIONS; TURNING; VIBRATION ANALYSIS; WEAR OF MATERIALS;

ACCELERATION SIGNALS; ARMA; AUTOREGRESSIVE MOVING AVERAGE; NATURAL MODES; TOOL WEAR; TOOL WEAR ESTIMATIONS; VIBRATION; VIBRATIONAL FEATURES;

AUTOREGRESSIVE MOVING AVERAGE MODEL;

EID: 84925488977     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-014-6296-3     Document Type: Article

References (39)

1. Martin KF (1994) A review by discussion of condition monitoring and fault diagnosis in machine tools. Int J Mach Tools Manufact 34(4):527–551
2. Ghasempoor A, Moore TN, Jeswiet J (1998) On-line wear estimation using neural networks. Proc Ins Mech Eng – B 212:105–112
3. Pandit S and M Kashou S. (August 1982) A data dependent systems strategy of on-line tool wear sensing. Journal of Engineering for Industry, Vol. 104/217
4. Liang S Y and Dornfeld D A. (August 1989) Tool wear detection using time series analysis of acoustic emission. Journal of Engineering for Industry, Vol. 111/199
5. Jemielniak K, Kossakowska J, Urbanski T (2011) Application of wavelet transform of acoustic emission and cutting force signals for tool condition monitoring in rough turning of Inconel 625. Proc IMechE Part B: J Eng Manuf 225:123–129
6. Li X (2002) A brief review: acoustic emission method for tool wear monitoring during turning. Int J Mach Tools Manuf 42:157–165
7. Yao Y, Fang XD (1992) Modelling of multivariate time series for tool wear estimation in finish-turning. Int J Mach Tools Manuf 32(4):495–508
8. Roth J T and Pandit S M. (November 1999) Monitoring end-mill wear and predicting tool failure using accelerometers. Journal of Manufacturing Science and Engineering, Vol. 121/559
9. Sick B (2002) Online and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech Syst Signal Process 16(4):487–546
10. Oraby SE, Hayhurst DR (2004) Tool life determination based on the measurement of wear and tool force ratio variation. Int J Mach Tools Manuf 44:1261–1269
11. Rehorn Adam G, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26:693–710
12. Szecsi T (1999) A DC motor based cutting tool condition monitoring system. J Mater Process Technol 92–93:350–354
13. Danai K, Ulsoy AG (1987) An adaptive observer for on-line tool wear estimation in turning. Part I: theory. Mech Syst Signal Proc l(2):211–225
14. Danai K, Ulsoy AG (1987) An adaptive observer for on-line tool wear estimation in turning. Part II: results. Mech Syst Signal Proc l(2):227–240
15. Huang SN, Tan KK, Wong YS et al (2007) Tool wear detection and fault diagnosis based on cutting force monitoring. Int J Mach Tools Manuf 47:444–451
16. Alonso FJ, Salgado DR (2008) Analysis of the structure of vibration signals for tool wear detection. Mech Syst Signal Process 22:735–748
17. Kilundu B, Dehombreux P, Chiementin X (2011) Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mech Syst Signal Process 25:400–415
18. Wang X, Wang W, Huang Y et al (2008) Design of neural network-based estimator for tool wear modelling in hard turning. J Intell Manuf 19:383–396
19. Aliustaoglu C, Ertunc H, Ocak H (2009) Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech Syst Signal Process 23:539–546
20. Purushothaman S (2010) Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns. J Intell Manuf 21:717–730
21. Sharma VS, Sharma SK, Sharma AK (2008) Cutting tool wear estimation for turning. J Intell Manuf 19:99–108
22. Srikant RR, Krishna PV, Rao ND (2011) Online tool wear prediction in wet machining using modified back propagation neural network. Proc IMechE Part B: J Eng Manuf 225:1009
23. Brezak D, Majetic D et al (2012) Tool wear estimation using an analytic fuzzy classifier and support vector machine. J Intell Manuf 23:797–809
24. Penedo F, Haber R E, Gajate A and Del Toro R M. (November 2012) Hybrid incremental modeling based on least squares and fuzzy-NN for monitoring tool wear in turning processes. IEEE transactions on industrial informatics, Vol. 8, No. 4.
25. Leem C S, Dornfeld D A and Dreyfus S E. (May 1995) A customized neural network for sensor fusion in on-line monitoring of cutting tool wear. Journal of Engineering for Industry, Vol. 117/153
26. Chen S L and Chang T H. (2001) Using a data fusion neural network in the tool wear monitoring of a computer numerical control turning machine. Proc Instn Mech Engrs Vol 215 Part B.
27. Segreto T, Simeone A, Teti R (2012) Sensor fusion for tool state classification in nickel superalloy high performance cutting. Procedia CIRP 1:593–598
28. Segreto T, Simeone A, Teti R (2013) Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion. Procedia CIRP 12:85–90
29. Paul PS, Varadarajan AS (2012) A multi-sensor fusion model based on artificial neural network to predict tool wear during hard turning. Proc IMechE Part B: J Eng Manuf 226(5):853–860
30. Jemielniak K, Urbanski T et al (2012) Tool condition monitoring based on numerous signal features. Int J Adv Manuf Technol 59:73–81
31. Liu TI, Song SD et al (2013) Online monitoring and measurements of tool wear for precision turning of stainless steel parts. Int J Adv Manuf Technol 65:1397–1407
32. Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65:371–393
33. Martin Richard J, (April 2000) A metric for ARMA processes, IEEE Transactions on Signal Processing, Vol. 48, No. 4.
34. De Cock K, De Moor B (2002) Subspace angles between ARMA models. Syst Control Lett 46(4):265–270
35. Zheng H, Mita A (2007) Damage indicator defined as the distance between ARMA models for structural health monitoring. Struct Control Health Monit. doi:10.1002/stc.235
36. Pandit SM, Wu SM (1983) Time series and system analysis with applications. Wiley, USA
37. Ostasevicius V, Gaidys R, Rimkeviciene J, Dauksevicius R (2010) An approach based on tool mode control for surface roughness reduction in high-frequency vibration cutting. J Sound Vib 329:4866–4879
38. Remadna M, Rigal JF (2006) Evolution during time of tool wear and cutting forces in the case of hard turning with CBN inserts. J Mater Process Technol 178:67–75
39. Cheng K. (2009) Machining dynamics fundamentals, applications and practices, Springer London, (pp 134–136).