Tool Wear Monitoring in Milling Processes Based on Cointegration Modeling

Yin Hu Cui; Guo Feng Wang; Dong Biao Peng

doi:10.4028/www.scientific.net/AMM.34-35.1746

Paper Titles

Design Theory of Profile Connection and Overview of Manufacturing Technology
p.1730

Design of an Images Security System
p.1735

Design of Multi-Channel Data Acquisition System Based on PXI 6281
p.1739

Research on Education of Creativity and Innovation
p.1742

Tool Wear Monitoring in Milling Processes Based on Cointegration Modeling
p.1746

Bifurcation Control for a Kind of Non-Autonomous System with Time Delay
p.1752

Research on Dynamic Characteristics of 750KW Wind Turbine Flexible Blades
p.1757

The Numerical Simulation of Aerodynamic Performance for Wind Turbines’ Blade Wheel
p.1761

Cotton Picker Picks Roller Motion Simulation and Dynamics Analysis
p.1765

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 34-35Tool Wear Monitoring in Milling Processes Based on...

Tool Wear Monitoring in Milling Processes Based on Cointegration Modeling

Abstract:

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, a reliable method is necessary for practical application. In this paper, a new method based on cointegration theory was introduced to extract features from the cutting force signal in the milling process. Cointegration relationship between cutting forces of different directions could be found and the corresponding cointegration vector could also be calculated. In order to improve the reliability of pattern recognition, the cointegration vectors combined with the energy of the high-frequency components of the acoustic emission signals were used as features. Once all the features are extracted, they were trained and tested through a support vector machine model. Experiments were performed to verify this method and the results showed that it could efficiently recognize the tool wear status.

You might also be interested in these eBooks

Mechanical Engineering and Green Manufacturing

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 34-35)

Pages:

1746-1751

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.34-35.1746

Citation:

Cite this paper

Online since:

October 2010

Authors:

Yin Hu Cui, Guo Feng Wang, Dong Biao Peng

Keywords:

Acoustic Emission (AE), Cointegration Modeling, Support Vector Machine (SVM), Tool Wear Monitoring

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] K. P. Zhu, Y. S. Wong, and G. S. Hong, Int. J. Mach. Tool. Manufact. Vol. 49, (2009), p.537.

Google Scholar

[2] R. Du, Eng. App Art. Int. Vol. 12, (1999), p.585.

Google Scholar

[3] K. Jemielniak and P. J. Arrazola, Int. J. Manu. Sci. Tech. Vol. 1, (2008), p.97.

Google Scholar

[4] X. L. Li, Int. J. Mach. Tool. Manufact. Vol. 42, (2002), p.157.

Google Scholar

[5] H. M. Ertunc, K. A. Loparo, H. Ocak, Int. J. Mach. Tool. Manufact. Vol. 41, (2001), p.1363.

Google Scholar

[6] D. F. Shi, N. N. Gindy, Mech. Sys. Sig. Pro. Vol. 21, (2007), p.1799.

Google Scholar

[7] N. Ghosh, Y. B. Ravi, A. Patra, S. Mukhopadhyay, S. Paul, A. R. Mohanty, A. B. Chattopadhyay, Mech. Sys. Sig. Pro. Vol. 21, (2007), p.466 Sharp Worn 0 0. 2 0. 4 0. 6 0. 8 1. 0 0 0. 2 0. 4 0. 6 0. 8 1. 0.

Google Scholar

[8] D. A. Dickey, W. A. Fuller, J. Am. Stat. A. Vol. 74, (1979), p.427.

Google Scholar

[9] S. Johansen, Econometrica Vol. 59, (1991), p.1551.

Google Scholar

[10] D. Haussler, Editor. Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, (1992), July 27-29; Pittsburgh, USA.

Google Scholar

[11] M. Malekian, S. S. Park, M. B. G. Jun, J. Mat. Pro. Tech. Vol. 209, (2009), p.4903.

Google Scholar