Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

Abstract:

Article Preview

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

Info:

Periodical:

Edited by:

Dongming Guo, Tsunemoto Kuriyagawa, Jun Wang and Jun’ichi Tamaki

Pages:

15-20

DOI:

10.4028/www.scientific.net/KEM.329.15

Citation:

X. Chen and J. Griffin, "Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours", Key Engineering Materials, Vol. 329, pp. 15-20, 2007

Online since:

January 2007

Export:

Price:

$38.00

[1] D. Royer, E. Dieulesaint: Elastic Waves in Solids I, II. (Springer-Verlag Berlin Heidelberg, New York, 2000).

[2] J. Webster, I. Marinescu, R. Bennett: Acoustic emission for process control and monitoring of surface integrity during grinding, Annals of the CIRP, Vol. 43/1 (1994), pp.299-304.

DOI: 10.1016/s0007-8506(07)62218-5

[3] M. Chen, B.Y. Xue: Study on acoustic emission in the grinding process automation, The ASME International Mechanical Engineering Congress and Exhibition, MED, (Nashville, TN, USA, ASME, Fairfield, NJ, USA. 14-19 Nov. 1999).

[4] Q. Liu, X. Chen, N. Gindy: Fuzzy pattern recognition of AE signals for grinding burn, International Journal of Machine Tools and Manufacture, Vol. 45/7-8 (2005), pp.811-818.

DOI: 10.1016/j.ijmachtools.2004.11.002

[5] P. R. De Aguiar, J. F. G. De Oliveira: Production grinding burn detection using acoustic emission and electric power signals, Abrasives (Dec 1998/Jan 1999), pp.8-11.

[6] Z. Wang, P. Willett, et al.: Neural network detection of grinding burn from acoustic emission, International Journal of Machine Tools and Manufacture, Vol. 41/2 (2001), pp.283-309.

DOI: 10.1016/s0890-6955(00)00057-2

[7] Q. Liu, X. Chen, N. Gindy: Investigation of acoustic emission signals under a simulative environment of grinding burn, International Journal of Machine Tools & Manufacture, Vol. 46 (2006), pp.284-292.

DOI: 10.1016/j.ijmachtools.2005.05.017

[8] X. Li: A brief review: Acoustic emission method for tool wear monitoring during turning, International Journal of Machine Tools and Manufacture, Vol. 42/2 (2002), pp.157-165.

DOI: 10.1016/s0890-6955(01)00108-0

[9] X. Li, J. Wu: Wavelet analysis of acoustic emission signals in boring, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 214/5 (2000), pp.421-424.

DOI: 10.1243/0954405001518206

[10] W.J. Staszewski, K.M. Holford: Signal processing of acoustic emission data, Key Engineering Materials, Vol. 204-205 (2001), pp.351-358.

DOI: 10.4028/www.scientific.net/kem.204-205.351

[11] M. Barbezat, A.J. Brunner, P. Flueler, C. Huber and X. Kornmann: Acoustic emission sensor properties of active fibre composite elements compared with commercial acoustic emission sensors, Sensors and Actuators Vol. 114 (2004), pp.13-20.

DOI: 10.1016/j.sna.2004.01.062

In order to see related information, you need to Login.