Monitoring of Grinding Burn by AE and Vibration Signals

Rodolpho F. Godoy Neto, Marcelo Marchi, Cesar Martins, Paulo R. Aguiar, Eduardo Bianchi

Abstract

The grinding process is widely used in surface finishing of steel parts and corresponds to one of the last steps in the manufacturing process. Thus, it’s essential to have a reliable monitoring of this process. In grinding of metals, the phenomenon of burn is one of the worst faults to be avoided. Therefore, a monitoring system able to identify this phenomenon would be of great importance for the process. Thus, the aim of this work is the monitoring of burn during the grinding process through an intelligent system that uses acoustic emission (AE) and vibration signals as inputs. Tests were performed on a surface grinding machine, workpiece SAE 1020 and aluminum oxide grinding wheel were used. The acquisition of the vibration signals and AE was done by means of an oscilloscope with a sampling rate of 2MHz. By analyzing the frequency spectra of these signals it was possible to determine the frequency bands that best characterized the phenomenon of burn. These bands were used as inputs to an artificial neural networks capable of classifying the surface condition of the part. The results of this study allowed characterizing the surface of the work piece into three groups: No burn, burn and high surface roughness. The selected neural model has produced good results for classifying the three patterns studied.

References

  1. Aguiar, P.R., Bianchi, E.C. & Oliveira, J.F.G., 2002. A method for burning detection in grinding process using acoustic emission and effective electric power signals. Manufacturing Systems, 31(3), pp.253-257.
  2. Aguiar, P.R. De, Paula, W.C.F. De & Bianchi, E.C., 2010. Analysis of Forecasting Capabilities of Ground Surfaces Valuation Using Artificial Neural Networks. J. of the Braz. Soc. of Mech. Sci. & Eng., XXXII(2), pp.146-153.
  3. Ahmadzadeh, F. & Lundberg, J., 2013. Remaining useful life prediction of grinding mill liners using an artificial neural network. Minerals Engineering, 53, pp.1-8.
  4. Axinte, D. a et al., 2004. Process monitoring to assist the workpiece surface quality in machining. International Journal of Machine Tools and Manufacture, 44(10), pp.1091-1108.
  5. Babel, R., Koshy, P. & Weiss, M., 2013. Acoustic emission spikes at workpiece edges in grinding: Origin and applications. International Journal of Machine Tools and Manufacture, 64, pp.96-101.
  6. Dimla, Snr., D.E., 2002. The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation. The International Journal of Advanced Manufacturing Technology, 19(10), pp.705- 713.
  7. Dotto, F. et al., 2003. Automatic detection of thermal damage in grinding process by artificial neural network. Revista Escola de Minas, 56(4), pp.295-300.
  8. Hassui, a. et al., 1998. Experimental evaluation on grinding wheel wear through vibration and acoustic emission. Wear, 217(1), pp.7-14.
  9. Hassui, a. & Diniz, a. E., 2003. Correlating surface roughness and vibration on plunge cylindrical grinding of steel. International Journal of Machine Tools and Manufacture, 43(8), pp.855-862.
  10. Irani, R. a., Bauer, R.J. & Warkentin, a., 2005. A review of cutting fluid application in the grinding process. International Journal of Machine Tools and Manufacture, 45(15), pp.1696-1705.
  11. Kim, H.. et al., 2001. Process monitoring of centerless grinding using acoustic emission. Journal of Materials Processing Technology, 111(1-3), pp.273-278.
  12. Kwak, J.-S. & Ha, M.-K., 2004. Neural network approach for diagnosis of grinding operation by acoustic emission and power signals. Journal of Materials Processing Technology, 147(1), pp.65-71.
  13. Liao, T.W. et al., 2008. Grinding wheel condition monitoring with boosted minimum distance classifiers. Mechanical Systems and Signal Processing, 22(1), pp.217-232.
  14. Liu, Q., Chen, X. & Gindy, N., 2005. Fuzzy pattern recognition of AE signals for grinding burn. International Journal of Machine Tools and Manufacture, 45(7-8), pp.811-818.
  15. Marzi, H., 2008. Modular Neural Network Architecture for Precise Condition Monitoring. IEEE Transactions on Instrumentation and Measurement, 57(4), pp.805- 812.
  16. Spadotto, M.M. et al., 2008. Classification of burn degrees in grinding by neural nets. In IASTED international conference on artificial intelligence and applications. Innsbruck: ACTA Press, pp. 1-6.
  17. Teti, R. et al., 2010. Advanced monitoring of machining operations. CIRP Annals - Manufacturing Technology, 59(2), pp.717-739.
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Paper Citation


in Harvard Style

F. Godoy Neto R., Marchi M., Martins C., R. Aguiar P. and Bianchi E. (2014). Monitoring of Grinding Burn by AE and Vibration Signals . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 272-279. DOI: 10.5220/0004753602720279


in Bibtex Style

@conference{icaart14,
author={Rodolpho F. Godoy Neto and Marcelo Marchi and Cesar Martins and Paulo R. Aguiar and Eduardo Bianchi},
title={Monitoring of Grinding Burn by AE and Vibration Signals},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={272-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004753602720279},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Monitoring of Grinding Burn by AE and Vibration Signals
SN - 978-989-758-015-4
AU - F. Godoy Neto R.
AU - Marchi M.
AU - Martins C.
AU - R. Aguiar P.
AU - Bianchi E.
PY - 2014
SP - 272
EP - 279
DO - 10.5220/0004753602720279