On-line monitoring of boring tools for control of boring operations

Robotics and Computer-Integrated Manufacturing

Volumn 26, Issue 3, 2010, Pages 230-239

  Liu, T I ^a   Kumagai, A ^a   Wang, Y C ^a   Song, S D ^a   Fu, Z ^a   Lee, J ^b  

^a CALIFORNIA STATE UNIVERSITY   (United States)

^b 705 Engineering Research Center   (United States)

Author keywords

Feature selection; Neuro fuzzy systems

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ARE BORING; AVERAGE VALUES; BACK PROPAGATION NEURAL NETWORKS; CUTTING FORCE SIGNALS; FEATURE SELECTION; FLANK WEAR; INPUT VECTOR; LONGITUDINAL FORCE; NEURAL COMPUTING; NEUROFUZZY SYSTEM; ON-LINE CLASSIFICATION; ON-LINE MEASUREMENT; ONLINE MONITORING; RADIAL FORCES; SEQUENTIAL FORWARD SEARCH ALGORITHMS; TANGENTIAL FORCE; TOOL CONDITION; TOOL WEAR; VIRTUAL INSTRUMENTATION;

BORING; CUSTOMER SATISFACTION; CUTTING; FEATURE EXTRACTION; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; MONITORING; QUALITY CONTROL; TOTAL QUALITY MANAGEMENT; VOLTAGE MEASUREMENT; WEAR OF MATERIALS; WELDS;

BORING TOOLS;

EID: 77950595707     PISSN: 07365845     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rcim.2009.11.002     Document Type: Article

References (25)

1. Chow J.G., and Kakade N.N. Finite element analysis of engine bore distortion during boring operation. Journal of Engineering for Industry 115 (1993) 379-384
2. Wu S.M., Eman K.F., and Kim K. In-process control of cylindricity in boring operations. Journal of Engineering for Industry 109 (1987) 291-296
3. Mackinnon R, Wilson GE, Wilkinson AJ, Tool condition monitoring using multi-component force measurement, MDTR Conference, 1986, p. 317-23.
4. Liu TI, Lee J, Gill GS, On-line monitoring and diagnosis of tapping process using neuro fuzzy systems. In: Proceedings of the 3rd IEEE international conference on industrial electronics and applications, Singapore, 2008, p. 1-5.
5. Liu T.I., Kumagai A., and Lee C. Enhancement of drilling safety and quality using on-line sensors and artificial neural networks. International Journal of Occupational Safety and Ergonomics 9 (2003)
6. Tarng Y.S., Hwang S.T., and Wang Y.S. A neural network controller for constant turning force. International Journal of Machine Tools and Manufacture 34 4 (1994) 453-460
7. Wu S.M., Elbestawi M.A., and Du R. Automated monitoring of manufacturing process. Journal of Engineering for Industry 117 (1995) 134-141
8. Das S., Roy R., and Chattpoadhyay A.B. Evaluation of wear of turning carbide inserts using neural networks. International Journal of Machine Tools and Manufacture 36 7 (1996) 789-797
9. Stein J.L., and Huh k. Monitoring cutting forces in turning: a model-based approach. ASME Journal of Manufacturing Science and Engineering 124 (2002) 26-31
10. Christensen D.M., and Wu S.M. Feasibility study of on-line surface finish prediction in boring operation. International Journal of Machine Tools Design and Research 22 4 (1982) 253-260
11. Del Taglia A., and Tani G. A method for measuring cutting forces in boring operations. International Journal of Machine Tools Design and Research 22 1 (1982) 20-30
12. Dornfeld D., and Rangwala S. Sensor integration using neural networks for intelligent tool condition monitoring. ASME Journal of Engineering for Industry 112 3 (1990) 219-228
13. HNC ExploreNet 3000, San Diego: HNC, Inc, 1991. 2-1.
14. Jang J.S., and Gulley N. Fuzzy logic toolbox for use with Matlab (1995), The Math Works, Inc., Natick, MA p. 2
15. Jang J.S. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on System, Man, and Cybernetics 23 3 (1993) 665-685
16. Kumagai A., Liu T.I., and Hozian P. Control of shape memory alloy actuators with a neuro-fuzzy feedforward model element. Journal of Intelligent Manufacturing 17 (2006) 45-56
17. Liu T.I., Ordukhani F., and Jani D. Monitoring and diagnosis of roller bearing conditions using neural networks and soft computing. International Journal of Knowledge-based and Intelligent Engineering Systems 9 (2005) 149-157
18. Devillez A., and Dudzinski D. Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers. Mechanical Systems and Signal Processing 21 (2007) 441-456
19. Johnson G.W. LabVIEW graphical programming, practical applications in instrumentation and control (1994), McGraw-Hill, Inc., New York
20. Fu L., Ling S.F., and Tseng C.H. On-line breakage monitoring of small drills with input impedance of driving motor. Mechanical Systems and Signal Processing 21 1 (2007) 457-465
21. Liu T.I., and Anantharaman K.S. Intelligent classification and measurement of drill wear. ASME Journal of Engineering for Industry 116 (1994) 392-397
22. Cover T.M. Geometrical and statistical properties of systems of linear inequalities with application in pattern recognition. IEEE Transaction on Computers 14 (1965) 326-334
23. Devijver P.A., and Kittler J. Pattern recognition - a statistical approach (1982), Prentice-Hall, New Jersey
24. Whitney A. A direct method of non-parametric measurement selection. IEEE Transaction on Computers 20 (1971) 1100-1103
25. ISO. Tool life testing with single-point turning tools, ISO/TC 29/WG 22 (Secretariat 37), 1972. p. 91.