Directionally Independent Failure Prediction of End-Milling Tools During Pocketing Maneuvers

[+] Author and Article Information
Christopher A. Suprock, Joseph J. Piazza

The Behrend College, Penn State Erie, Erie, PA 16563

John T. Roth

The Behrend College, Penn State Erie, Erie, PA 16563jtr11@psu.edu

J. Manuf. Sci. Eng 129(4), 770-779 (Jan 23, 2007) (10 pages) doi:10.1115/1.2738116 History: Received July 07, 2006; Revised January 23, 2007

Tracking the health of cutting tools under typical wear conditions is advantageous to the speed and efficiency of manufacturing processes. Existing techniques monitor tool performance through analyzing force or acceleration signals whereby prognoses are made from a single sensor type. This work proposes to enhance the spectral output of autoregressive (AR) models by combining triaxial accelerometer and triaxial dynamometer signals. Through parallel processing of force and acceleration signals using single six degree of freedom modeling, greater spectral resolution is achieved. Two entirely independent methods of tracking the tool wear are developed and contrasted. First, using the discrete cosine transform, primary component analysis will be applied to the spectral output of each AR auto and cross spectrum (Method 1). Each discrete cosine transform of the six-dimensional spectral data is analyzed to determine the magnitude of the critical (primary) variance energy component of the respective spectrum. The eigenvalues of these selected spectral energies are then observed for trends toward failure. The second method involves monitoring the eigenvalues of the spectral matrices centered at the toothpass frequency (Method 2). The results of the two methodologies are compared. Through the use of the eigenvalue method, it is shown that, for straight and pocketing maneuvers, both methods successfully track the condition of the tool using statistical thresholding. The techniques developed in this work are shown to be robust by multiple life tests conducted on different machine platforms with different operating conditions. Both techniques successfully identify impending fracture or meltdown due to wear, providing sufficient time to remove the tools before failure is realized.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 4

Typical tool wear curve showing confidence intervals

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Figure 5

Pocketing workpiece orientation

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Figure 2

Tool path during pocketing

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Figure 3

6×6× spectral output from AR model

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Figure 6

Linear cut workpiece orientation

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Figure 7

Trends from DCT method

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Figure 8

Unprocessed spectral magnitudes at toothpass frequency

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Figure 9

Trends from TPF method

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Figure 10

Appendix 1: Trend using FFT model. Appendix 2: Trend using AR model.



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