Influence of Sensor Positioning in Tool Condition Monitoring of Drilling Process through Vibration Analysis

Article Preview

Abstract:

In this study, the relationship between vibration and tool wear and also influence of sensor positioning in tool codition monitoring were investigated during drilling. For this purpose, a series of experiment were conducted in a CNC vertical milling machine using drilling cycle. A 6 mm diameter HSS drill and EN24 as workpiece material were used in these experiments. The vibration was measured in the transverse direction of sensor which is positioned on the workpiece with constant distance from the holes to be drilled for monitoring tool wear as in previous studies. But, positioning of sensor in a constant place with equal distance from all holes to be drilled is not possible for all the workpiece profiles in actual practice. Experiments show that the distance of sensor from the holes in drilling affects the vibration signals for the same state of wear.It shows that the tool wear models presented in previous studies using acceleration signals are sensor location dependent. This work presents a Variance-amplitude of the vibration signals received for tool condition monitoring which is the most sensitive statistical parameter than other statistical parameters such as Root Mean Square (RMS), Exponential, Peak, max-min, mean and standard deviation. Results showed that there was no considerable increase in the vibration amplitude of variance until flank wear value of 0.30 mm was reached, above which the vibration amplitude increased significantly.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 984-985)

Pages:

564-569

Citation:

Online since:

July 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] H.M. Ertunc, K.A. Loparo, A decision fusion algorithm for tool wear condition monitoring in drilling, International Journal of Machine Tools & Manufacture, 41 (2001) 1347 – 1362.

DOI: 10.1016/s0890-6955(00)00111-5

Google Scholar

[2] Slavko Dolinsek, Janez Kopac, Acoustic emission signals for tool wear identification, WEAR, 225-229 (1999) 295-303.

DOI: 10.1016/s0043-1648(98)00363-9

Google Scholar

[3] Issam Abu – Mahfouz , Drilling wear detection and classification using vibration signals and artificial neural network, International Journal of Machine Tools & Manufacture, 43 (2003) 707-720.

DOI: 10.1016/s0890-6955(03)00023-3

Google Scholar

[4] T.I. El-Wardany, D. Gao and M.A. Elbestawi, Tool condition monitoring in drilling using vibration signature analysis, International Journal Machine Tools and Manufacture, 36 (1996) 687–711.

DOI: 10.1016/0890-6955(95)00058-5

Google Scholar

[5] Nakandhrakumar. R.S. Dinakaran. D, S. Satishkumar, J. Pattabiraman, Normalization of Distance variation in sensor positioning for Tool Condition Monitoring through Vibration analysis in Drilling, Proc. of International conference on Computer Aided Engineering 2013, Indian Institute of Technology Madras, Chennai, India (2013).

DOI: 10.4028/www.scientific.net/amr.984-985.564

Google Scholar

[6] S.S. Panda, D. Chakraborty, S. K. Pal, Flank wear prediction in drilling using back propagation neural network and radial basis function network, Applied Soft Computing, 8 (2008) 858-871.

DOI: 10.1016/j.asoc.2007.07.003

Google Scholar

[7] K. Patra, K. Pal, K. Bhattacharyya, ANN based prediction of drill flank wear from motor current signals, Applied soft computing, 7 (2007) 929-935.

DOI: 10.1016/j.asoc.2006.06.001

Google Scholar

[8] D. Dinakaran, S. Sampathkumar and N. Sivashanmugam, An experimental investigation on monitoring of crater wear in turning using ultrasonic technique, International Journal of Machine Tools & Manufacture, 49 (2009)1234 -1237.

DOI: 10.1016/j.ijmachtools.2009.08.001

Google Scholar