Paper Titles
Design and Simulation of Thermopile Sensor Technology Based on Porous Silicon
p.289
Fault Diagnosis with Mult-Signal Test for Reciprocating Compressor
p.294
The Design of Automobile Wheel Hub Runout Online Detection System
p.298
The Determination of Thermocouples Optimum Number in the Blast Furnace Hearth for Control of its Condition
p.302
Multi-Sensor Intelligent Monitoring of High-Speed Grinding for Brittle and Hard Materials
p.309
The Method of Dynamic Measurement in Light Distribution Performance Test
p.315
Research and Development of Machine Vision Detection System for Transparent Paper Package
p.319
Early Visualization Detection of Gray Mold (Botrytis) on Eggplant Leaves Based on Multi-Spectral Image
p.323
Design of the Elevator Wireless Monitoring System Based on LabVIEW
p.328

Multi-Sensor Intelligent Monitoring of High-Speed Grinding for Brittle and Hard Materials

Article Preview

Abstract:

This paper deals with an intelligent multi-sensor monitoring system, which focus on the characteristic of transient occurrence in high speed grinding and its application to the machining of brittle and hard materials. Different sensors are used to collect workpiece vibration, acoustic emission, force and displacement signals, which are used to define the stability of grinding process and monitoring the fault in high speed machining. Although there is a lot of methods have been reported in recent literature for monitoring grinding process, they have not a systematic method which can totally reflect the characteristic of high speed grinding. On the other hand，no single sensor or feature has been shown to be successfully and precisely all grinding faults. This paper combined different feature selection including time-frequency domain or wavelet methods and sensor fusion based on clustering method to deal with the stability condition test in high-speed grinding. The validity of the proposed method and the excellent detection accuracy is demonstrated through tests with SiC machining in high-speed grinding.

You might also be interested in these eBooks
Engineering Solutions for Manufacturing Processes V View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 741)

Pages:

309-314

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.741.309

Citation:

Cite this paper

Online since:

March 2015

Authors:

Yin Chen Ma, Jian Guo Yang

Keywords:

Feature Selection, Grinding Process, Monitoring, Multi-Sensor, Sensor Fusion

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] M. Z. NUAWI, F. LAMIN, M. J. M. NOR, N. JAMALUDDIN, S. ABDULLAH. Clustering of Machining Signal for Verifying Machining Parameter. Proceedings of the 1st WSEAS International Conference on SENSORS and SIGNALS (SENSIG '08).

Google Scholar

[2] Sanjay Agarwal, P. Venkateswara Rao. Grinding characteristics, material removal and damage formation mechanisms in high removal rate grinding of silicon carbide. International Journal of Machine Tool& Manufacture. 2010. 9.

DOI: 10.1016/j.ijmachtools.2010.08.008

Google Scholar

[3] H.K. Tonshoff, T. Friemuth, J. C. Becker. Process monitoring in grinding. CIRP Annals - Manufacturing Technology, Volume 51, Issue 2, 2002, Pages 551-571.

DOI: 10.1016/s0007-8506(07)61700-4

Google Scholar

[4] T. jayakumar, C. K Mukhopadhyay, S. Venugopal, S. L Mannan, Baldev Raj. A review of the application of acoustic emission techniques for monitoring forming and grinding processes. Journal of Materials Processing Technology. 159(2005)48-61.

DOI: 10.1016/j.jmatprotec.2004.01.034

Google Scholar

[5] T.W. Hwang, E.P. Whitenton, N.N. Hsu, G.V. Blessing, C.J. Evans, Acoustic emission monitoring of high speed grinding of silicon nitride, Ultrasonics 38 (2000) 614–619.

DOI: 10.1016/s0041-624x(99)00064-5

Google Scholar

[6] P. Lezanski, An intelligent system for grinding wheel condition monitoring, Journal of Materials Processing Technology 109 (2001)258–263.

DOI: 10.1016/s0924-0136(00)00808-6

Google Scholar

[7] M ALEYAASIN, MEBRHIMI and R WHALLEY. Multivariable control of AMB spindles. Multi-body Dynamics: Monitoring and Simulation Techniques-II. 305. (2000).

Google Scholar

[8] Niranjan Subrahmanya, Yun C. Shin. Automated sensor selection and fusion for monitoring and diagnostics of plunge grinding. Journal of manufacturing science and engineering. 2008. 6.

DOI: 10.1115/1.2927439

Google Scholar

[9] J. Kopac, P. Krajnik. High-performance grinding—A review. Journal of Materials Processing Technology 175 (2006) 278–284.

DOI: 10.1016/j.jmatprotec.2005.04.010

Google Scholar

[10] I. Inasaki. Sensor fusion for monitoring and controlling grinding processes. The international journal of advanced manufacturing technology. (1999).

Google Scholar

[11] Alex W. Moerlein. Eric R. Marsh. Theodore R.S. Deakyne.R. Ryan Vallance. In-process force measurement for diameter control in precision cylindrical grinding. Int J Adv Manuf Technol. (2009)42: 93-101.

DOI: 10.1007/s00170-008-1584-4

Google Scholar

[12] Julie Z. Zhang. Joseph C. Chen. Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system. Int J Adv Manuf Techol (2008) 39: 118-128.

DOI: 10.1007/s00170-007-1186-6

Google Scholar

[13] Shunuan Liu & Zhenming Zhang & Xitian Tian. A typical process route discovery method based on clustering analysis. Int J Adv Manuf Technol (2007) 35: 186–194.

DOI: 10.1007/s00170-006-0706-0

Google Scholar