Using the Eigenvalues of Multivariate Spectral Matrices to Achieve Cutting Direction and Sensor Orientation Independence

[+] Author and Article Information
John T. Roth

School of Engineering and Engineering Technology, The Behrend College, Pennsylvania State University at Erie, 5091 Station Road, Erie, PA 16563-1701

J. Manuf. Sci. Eng 128(1), 350-354 (May 20, 2005) (5 pages) doi:10.1115/1.2123067 History: Received January 06, 2003; Revised May 20, 2005

There is a strong need for monitoring techniques capable of tracking the health of cutting tools under varying conditions. Unfortunately, most monitoring techniques are dependent on the cutting direction and/or the sensor orientation, limiting their effectiveness in the typical industrial environment. With this in mind, this research develops a monitoring technique that is independent of both of these factors. This is accomplished by using multivariate autoregressive models that are fit to the output from a triaxial accelerometer. The work shows that the eigenvalues of multivariate spectral matrices, calculated at the machining frequencies, are not only sensitive to the condition of the tool but are also independent of the direction of cutting and the orientation of the sensor. This independence is verified experimentally through tests conducted under a variety of cutting directions and sensor orientations.

Copyright © 2006 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 1

Workpiece and accelerometer orientations for the first series of experiments

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Figure 2

Accelerometer orientations for the second series of experiments

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Figure 3

Workpiece orientations for the third series of experiments



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