Tool condition monitoring of single-point dresser using acoustic emission and neural networks models

IEEE Transactions on Instrumentation and Measurement

Volumn 63, Issue 3, 2014, Pages 667-679

  Martins, Cesar H R ^a   Aguiar, Paulo R ^a   Frech, Arminio ^a   Bianchi, Eduardo Carlos ^a  

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

Author keywords

Acoustic emission (AE); dressing operation; manufacturing automation; neural network application; tool wear condition

Indexed keywords

ACOUSTIC EMISSIONS; FREQUENCY BANDS; WEAR OF MATERIALS;

ACOUSTIC EMISSION SIGNAL; CLASSIFICATION ABILITY; MANUFACTURING AUTOMATION; NEURAL NETWORK APPLICATION; NEURAL NETWORKS MODEL; PROJECT SPECIFICATION; TOOL CONDITION MONITORING; TOOL WEAR;

NEURAL NETWORKS;

EID: 84894524789     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2013.2281576     Document Type: Article

References (22)

1. A. T. Nguyen and D. L. Butler, "Correlation of grinding wheel topography and grinding performance: A study from a ciewpoint of three-dimensional surface characterisation, " J. Mater. Process. Technol., vol. 208, no. 1, pp. 14-23, 2008.
2. K. Wegener, H.-W. Hoffmeister, B. Karpuschewski, F. Kuster, W.-C. Hahmann, and M. Rabiey, "Conditioning and monitoring of grinding wheels, " CIRP Ann., Manuf. Technol., vol. 60, no. 2, pp. 757-777, 2011.
3. J. Porzycki, "Surface-roughness model in traverse grinding, " Archives Civil Mech. Eng., vol. 5, no. 1, pp. 5-12, 2005.
4. W. Habrat, A. Batsch, and J. Porzycki, "Monitoring of the single-point diamond dresser wear, " Archives Civil Mech. Eng., vol. 5, no. 1, pp. 13-18, 2005.
5. J. H. Zhou, C. K. Pang, Z. W. Zhong, and F. L. Lewis, "Tool wear monitoring using acoustic emissions by dominant-feature identification, " IEEE Trans. Instrum. Meas., vol. 60, no. 2, pp. 547-559, Feb. 2011.
6. I. Egaña, A. Mendikute, X. Urionaguena, and H. Alberdi, "Towards intelligent dressing, " Instrum. Meas. Mag., vol. 9, no. 3, pp. 38-43, 2006. (Pubitemid 43898847)
7. I. Abu-Mahfouz, "Drilling wear detection and classification using vibration signals and artificial neural network, " Int. J. Mach. Tools Manuf., vol. 43, no. 7, pp. 707-720, 2003.
8. D. E. Lee, I. Hwang, C. M. O. Valente, J. F. G. Oliveira, and D. A. Dornfeld, "Precision manufacturing process monitoring with acoustic emission, " Int. J. Mach. Tools Manuf., vol. 46, no. 2, pp. 176-188, 2006. (Pubitemid 41563982)
9. X. Li, "A brief review: Acoustic emission method for tool wear monitoring during turning, " Int. J. Mach. Tools Manuf., vol. 42, no. 2, pp. 157-165, 2002. (Pubitemid 33018898)
10. Z. Wang, P. Willett, P. R. Aguiar, and J. Webster, "Neural network detection of grinding burn from acoustic emission, " Int. J. Mach. Tools Manuf., vol. 41, no. 2, pp. 283-309, 2001. (Pubitemid 32034815)
11. J. P. Davim, Machining: Fundamentals and Recent Advances, 2nd ed. London, U.K.: Springer-Verlag, 2008.
12. S. Haykin, Neural Networks and Learning Machine, 3rd ed. Englewood Cliffs, NJ, USA: Prentice-Hall, 2008.
13. H. Marzi, "Modular neural network architecture for precise condition monitoring, " IEEE Trans. Instrum. Meas., vol. 57, no. 4, pp. 805-812, Apr. 2008. (Pubitemid 351460472)
14. S. Chen and Y. W. Jen, "Data fusion neural network for tool condition monitoring in CNC milling machining, " Int. J. Mach. Tools Manuf., vol. 40, no. 3, pp. 381-400, 2000. (Pubitemid 30506057)
15. A. Prateepasen, Y. H. J. Au, and B. E. Jones, "Acoustic emission and vibration for tool wear monitoring in single-point machining using belief network, " in Proc. 18th IEEE Instrum. Meas. Technol. Conf., May 2001, pp. 1541-1546. (Pubitemid 32883738)
16. D. Shi, D. A. Axinte, and N. N. Gindy, "Online machining process monitoring using wavelet transform and SPC, " in Proc. IMTC, 2006, pp. 2081-2086. (Pubitemid 350089690)
17. X. Chuangwen, L. Zhe, and L. Wencui, "A frequency band energy analysis of vibration signals for tool condition monitoring, " in Proc. Int. Conf. Meas. Technol. Mech. Autom., 2009, pp. 385-388.
18. M. Kious, A. Ouahabi, M. Boudraa, R. Serra, and A. Cheknane, "Detection process approach of tool wear in high speed milling, " Measurement, vol. 43, no. 10, pp. 1439-1446, 2010.
19. X. Li, A. Djordjevich, and P. K. Venuvinod, "Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring, " IEEE Trans. Ind. Electron., vol. 47, no. 3, pp. 697-702, Jun. 2000.
20. X. Li, H.-X. Li, X.-P. Guan, and R. Du, "Fuzzy estimation of feed-cutting force from current measurement-A case study on intelligent tool wear condition monitoring, " IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 34, no. 4, pp. 506-512, Nov. 2004.
21. J.-H. Zhou, C. K. Pang, F. L. Lewis, and Z.-W. Zhong, "Intelligent diagnosis and prognosis of tool weear using dominant feature identification, " IEEE Trans. Ind. Inf., vol. 5, no. 4, pp. 454-464, Nov. 2009.
22. V. Franc and V. Hlavác, "Statistical pattern recognition toolbox for Matlab-User's guide, " Dept. Cybernetics, Czech Tech. Univ., Prague, Czech Republic, Tech. Rep. ISSN 1213-2365, 2004.