Design of multisensor fusion-based tool condition monitoring system in end milling

International Journal of Advanced Manufacturing Technology

Volumn 46, Issue 5-8, 2010, Pages 681-694

  Cho, Sohyung ^a   Binsaeid, Sultan ^b   Asfour, Shihab ^b  

^a SOUTHERN ILLINOIS UNIVERSITY EDWARDSVILLE   (United States)

^b UNIVERSITY OF MIAMI   (United States)

Author keywords

Machine ensemble; Multisensor fusion; Tool condition monitoring

Indexed keywords

ACOUSTIC EMISSION SENSORS; CLASSIFICATION METHODS; DECISION LEVEL FUSION; DESIGN COMPONENT; END MILL; END MILLING; ENSEMBLE TECHNIQUES; FEATURE LEVEL FUSION; FEATURE SELECTION METHODS; FEED BACK INFORMATION; FREQUENCY-DOMAIN DATA; FUSION METHODS; MAJORITY VOTING; MULTI SENSOR; MULTI-SENSOR FUSION; MULTIPLE SENSORS; PROCESS CONTROLLERS; REDUCED COMPLEXITY; SPINDLE POWER; TOOL CONDITION; TOOL CONDITION MONITORING;

ACOUSTIC EMISSIONS; ACOUSTICS; CARBIDES; DAMPING; FEATURE EXTRACTION; MACHINE COMPONENTS; MACHINE DESIGN; MACHINE TOOLS; MARTENSITIC STAINLESS STEEL; SENSORS; SIGNAL PROCESSING;

CONDITION MONITORING;

EID: 74249109730     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-009-2110-z     Document Type: Article

References (34)

1. RL Kegg 1984 On-line machine and process diagnostics Ann CIRP 32 2 469 473
2. S Kurada C Bradley 1997 A review of machine vision sensors for tool condition monitoring Comput Ind 34 55 72 10.1016/S0166-3615(96)00075-9 10.1016/S0166-3615(96)00075-9
3. MA Elbestawi TA Papazafiriou RX Du 1991 Process monitoring of tool wear in milling using cutting force signature Int J Mach Tools Manuf 31 1 55 73 10.1016/0890-6955(91)90051-4 10.1016/0890-6955(91)90051-4
4. BY Lee YS Tarng 1999 Milling cutter breakage detection by discrete wavelet transform Mechatronics 9 225 234 10.1016/S0957-4158(98)00049-X 10.1016/S0957-4158(98)00049-X
5. P Bhattacharyya D Senupta S Mukhopadhaya 2007 Cutting force based real-time estimation of tool wear in face milling using a combination of signal processing techniques Mech Syst Signal 21 6 2665 2683 10.1016/j.ymssp. 2007.01.004 10.1016/j.ymssp.2007.01.004
6. JC Chen W Chen 1999 Tool breakage detection system using accelerometer sensor J Intell Manuf 10 187 197 10.1023/A:1008980821787 10.1023/A:1008980821787
7. I Yesilyurt H Ozturk 2007 Tool condition monitoring in milling using vibration analysis Int J Prod Res 45 4 1013 1028 10.1080/00207540600677781 1128.90481 10.1080/00207540600677781 (Pubitemid 46270638)
8. L Atlas M Ostendorf GD Bernard 2000 Hidden Markov models for monitoring machining tool wear IEEE International Conference on Acoustics, Speech, and Signal Processing 6 3887 3890
9. IN Tansel ME Trujillo WY Bao 2001 Acoustic emission-based tool breakage detector for micro-end milling operations Int J Model Simul 21 1 10 16
10. N Ghosh YB Ravi A Patra S Mukhopadhyay S Paul AR Mohanty AB Chattopadhyay 2007 Estimation of tool wear during CNC milling using neural network based sensor fusion Mech Syst Signal Process 21 466 479 10.1016/j.ymssp. 2005.10.010 10.1016/j.ymssp.2005.10.010
11. S Cho S Asfour A Onar N Kaundinya 2005 Tool breakage detection using support vector machine learning in a milling process Int J Mach Tools Manuf 45 3 241 249 10.1016/j.ijmachtools.2004.08.016 10.1016/j.ijmachtools.2004.08.016
12. P Norman A Kaplan M Rantatalo I Svenningsson 2007 Study of a sensor platform for monitoring machining of aluminum and steel Meas Sci Technol 18 1155 1166 10.1088/0957-0233/18/5/001 10.1088/0957-0233/18/5/001 (Pubitemid 46591468)
13. Reddy YB (1992) Multisensor data fusion: state of the art. J Inf Sci Technol
14. AG Rehorn J Jiang PE Orban 2005 State-of-the-art methods and results in tool condition monitoring: a review Int J Adv Manuf Technol 26 693 710 10.1007/s00170-004-2038-2 10.1007/s00170-004-2038-2 (Pubitemid 41444225)
15. MA Elbestawi J Marks T Papazafiriou 1989 Process monitoring in milling by pattern recognition Mech Syst Signal Process 3 3 305 315 10.1016/0888-3270(89) 90055-1 10.1016/0888-3270(89)90055-1
16. RG Silva RL Reuben SJ Wilcox 1998 Tool wear monitoring of turning operation by neural network and expert system classification of a feature set generated from multiple sensors Mech Syst Signal Process 12 2 319 332 10.1006/mssp. 1997.0123 10.1006/mssp.1997.0123
17. B Sick 2002 On-line 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 10.1006/mssp. 2001.1460 10.1006/mssp.2001.1460
18. D Brezak T Udiljak D Majetic B Novakovic J Kasac 2004 Tool wear monitoring using radial basis function neural network Proc IEEE Int Jt Conf Neural Netw 3 1859 1862
19. S Yuan F Chu 2006 Support vector machines-based fault diagnosis for turbo-pump rotor Mech Syst Signal Process 20 939 952 10.1016/j.ymssp. 2005.09.006 10.1016/j.ymssp.2005.09.006 (Pubitemid 43121167)
20. Hall MA (1999) Correlation-based feature selection for machine learning. PhD dissertation, The University of Waikato, New Zealand
21. UM Fayyad KB Irani 1993 Multi-interval discretization of continuous-valued attributes for classification learning IJCA 93 1022 1027
22. R Kohavi GH John 1997 Wrappers for feature subset selection Artif Intell 97 273 324 10.1016/S0004-3702(97)00043-X 0904.68143 10.1016/S0004-3702(97)00043- X
23. Hong SJ, Raman M, Wong YS (2002) Feature extraction and selection in tool condition monitoring system. Lecture notes in computer science. Springer, Berlin, pp 487-497
24. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco 2005
25. VN Vapnik 1999 An overview of statistical learning theory IEEE Trans Neural Netw 10 5 988 999 10.1109/72.788640 10.1109/72.788640
26. Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Boston
27. Bishop CM (2006) Pattern recognition and machine learning. Springer Science and Business Media, LLC., New York
28. J Kittler M Hatef R Duin J Matas 1998 On combining classifiers IEEE Trans Pattern Anal Mach Intell 20 3 226 239 10.1109/34.667881 10.1109/34.667881
29. LK Hansen P Salamon 1990 Neural network ensembles IEEE Trans Pattern Anal Mach Intell 12 10 993 1001 10.1109/34.58871 10.1109/34.58871
30. TG Dietterich 2000 An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization Mach Learn 40 2 139 157 10.1023/A:1007607513941 10.1023/A:1007607513941 (Pubitemid 30891936)
31. Bouckaert RR (2003) Choosing between two learning algorithms based on calibrated test. Proceedings of 20th International Conference on Machine Learning, Morgan Kauffmann
32. P Palanisamy I Rajendran S Shanmugasundaram 2008 Prediction of tool wear using regression and ANN models in end-milling operation Int J Adv Manuf Technol 37 29 41 10.1007/s00170-007-0948-5 10.1007/s00170-007-0948-5 (Pubitemid 351651596)
33. DH Wolpert 1992 Stacked generalization Neural Netw 5 241 259 10.1016/S0893-6080(05)80023-1 10.1016/S0893-6080(05)80023-1
34. Seewald AK (2002) How to make stacking better and faster while also taking care of an unknown weakness. Proceedings of the 19th International Conference on Machine Learning, San Francisco, California