Online monitoring and measurements of tool wear for precision turning of stainless steel parts

International Journal of Advanced Manufacturing Technology

Volumn 65, Issue 9-12, 2013, Pages 1397-1407

  Liu, Tien I ^a,d   Song, Shin Da ^a   Liu, George ^b   Wu, Zhang ^c  

^a CALIFORNIA STATE UNIVERSITY   (United States)

^b Mori Saiki Manufacturing Inc   (United States)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^d NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Feature selection; Neural networks; Neuro fuzzy systems

Indexed keywords

BACKPROPAGATION; CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; NEURAL NETWORKS; PARTIAL DISCHARGES; STAINLESS STEEL; TURNING; WEAR OF MATERIALS;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; BACK PROPAGATION NEURAL NETWORKS; CATASTROPHIC FAILURES; NEUROFUZZY SYSTEM; ON-LINE CLASSIFICATION; ON-LINE MEASUREMENT; ON-LINE MONITORING AND MEASUREMENT; SEQUENTIAL FORWARD SEARCH ALGORITHMS;

CUTTING TOOLS;

EID: 84880571254     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-012-4265-2     Document Type: Article

References (31)

1. Atluru S, Huang SH, Snyder JP (2012) A Smart Machine Supervisory System Framework. Int J Adv Manuf Technol 58(5):563-572
2. Tansel IN, Li M, Demetgul M, Bickraj K, Kaya B, Ozcelik B (2012) Detecting chatter and estimating wear from the torque of end milling signals by using index based reasoner. Int J Adv Manuf Technol 58(1):109-118
3. Lee S, Li L, Ni J (2010) Online degradation assessment and adaptive fault detection using modified hidden Markov model. ASME J Manuf Sci Eng 132:021010-1-021010-11
4. Liu TI, Kumagai A, Wang YC, Song SD, Fu Z, Lee J (2010) On-line monitoring of boring tools for control of boring operations. Int J Robot Comput Integr Manuf 26(3):230-239
5. Zheng L, Yang XM, Zhang ZH, Liu TI (2008) A Web-based machining parameter selection system for life cycle cost reduction and product quality enhancement. Comput Ind 59:254-261
6. Liu TI, Kumagai A, Lee C (2003) Enhancement of drilling safety and quality using online sensors and artificial neural networks. Int J Occup Saf Ergon 9:37-56
7. Filho JMC (2012) Prediction of cutting forces in mill turning through process simulation using a five-axis machining center. Int J Adv Manuf Technol 58(1):71-80
8. Kalvoda T, Hwang YR (2010) A cutter tool monitoring using Hilbert-Huang transform. Int J Mach Tools Manuf 50:495-501
9. Suprock CA, Roth JT, Downey LM (2009) Endmill condition monitoring and failure forecasting method for curvilinear cuts of nonconstant radii. ASME J Manuf Sci Eng 131:021003-1-021003-8
10. Choi YJ, Park MS, Chu CN (2008) Prediction of drill failure using features extraction in time and frequency domains of feed motor current. Int J Mach Tools Manuf 48:29-39
11. Jemielniak K, Urbanski T, Kossakowska J, Bombbinski S (2012) Tool condition monitoring based on numerous signal features. Int J Adv Manuf Technol 59(1):73-81
12. Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Int J Robot Comput Integr Manuf 25(6):989-998
13. Devillez A, Dudzinski D (2008) Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers. Mech Syst Signal Process 21:441-456
14. Li X (2001) Real-time tool wear condition monitoring in turning. Int J Prod Res 39:981-992
15. ISO (1972) Tool life testing with single-point turning tools. ISO/TC 29/WG 22 (Secretariat 37) 91
16. Liu TI, Chen WY, Anantharaman KS (1998) Intelligent detection of drill wear. Mech Syst Signal Process 12(6):863-873
17. Johnson GW (1994) Practical applications in instrumentation and control, Labview graphical programming. McGraw-Hill, Inc., New York
18. Dornfeld D, Rangwala S (1990) Sensor integration using neural networks for intelligent tool condition monitoring. ASME J Eng Ind 112:219-228
19. Niu YM, Wong YS, Hong GS, Liu TI (1998) Multi-category classification of tool conditions using wavelet packets and ART2 network. ASME J Manuf Sci Eng 120:807-816
20. Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with application in pattern recognition. IEEE Trans Comput 14:326-334
21. Devijver PA, Kittler J (1982) Pattern recognition - a statistical approach. Prentice-Hall, New Jersey
22. Whitney (1971) A direct method of non-parametric measurement selection. IEEE Trans Comput 20:100-1103
23. HNC Inc (1991) HNC ExploreNet 3000. HNC Inc., San Diego
24. Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21:466-479
25. Rajsiri V, Lorre JP, Benaben F, Pingaud H (2010) Knowledge-based system for collaborative process specification. Comput Ind 61:161-175
26. Liu TI, Ordukhani F, Jani D (2005) Monitoring and diagnosis of roller bearing conditions using neural networks and soft computing. Int J Knowl-Based Intell Eng Syst 9:149-158
27. Nagata F, Kusumoto Y, Watanabe K (2009) Intelligent machining system for the artistic design of wooden paint rollers. Int J Robot Comput Integr Manuf 25(3):680-688
28. Jang JS, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River
29. Kumagai A, Liu TI, Hozian P (2006) Control of shape memory alloy actuators with a neuro-fuzzy feedforward model element. J Intell Manuf 17:45-56
30. He W, Zhang YF, Lee KS, Liu TI (2001) Development of a fuzzy-neuro system for parameter resetting of injection molding. ASME J Manuf Sci Eng 123:110-118
31. Liu TI, Ko EJ, Lee J (1993) Intelligent control of dynamic systems. J Franklin Inst 330(3):491-503