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.
Skip Nav Destination
Article navigation
August 2007
Technical Papers
Directionally Independent Failure Prediction of End-Milling Tools During Pocketing Maneuvers
Christopher A. Suprock,
Christopher A. Suprock
The Behrend College,
Penn State Erie
, Erie, PA 16563
Search for other works by this author on:
Joseph J. Piazza,
Joseph J. Piazza
The Behrend College,
Penn State Erie
, Erie, PA 16563
Search for other works by this author on:
John T. Roth
John T. Roth
Search for other works by this author on:
Christopher A. Suprock
The Behrend College,
Penn State Erie
, Erie, PA 16563
Joseph J. Piazza
The Behrend College,
Penn State Erie
, Erie, PA 16563
John T. Roth
J. Manuf. Sci. Eng. Aug 2007, 129(4): 770-779 (10 pages)
Published Online: January 23, 2007
Article history
Received:
July 7, 2006
Revised:
January 23, 2007
Citation
Suprock, C. A., Piazza, J. J., and Roth, J. T. (January 23, 2007). "Directionally Independent Failure Prediction of End-Milling Tools During Pocketing Maneuvers." ASME. J. Manuf. Sci. Eng. August 2007; 129(4): 770–779. https://doi.org/10.1115/1.2738116
Download citation file:
Get Email Alerts
Cited By
A Study on the Influence of Polypropylene Melt Flow Index on Nonwoven Fibers Produced Through Hot Melt Centrifugal Spinning
J. Manuf. Sci. Eng (April 2025)
Arc Characteristics of Aluminum Alloy Double-Wire High-Frequency Pulsed GMAW
J. Manuf. Sci. Eng (April 2025)
Related Articles
A Geometrical Simulation System of Ball End Finish Milling Process and Its Application for the Prediction of Surface Micro Features
J. Manuf. Sci. Eng (February,2006)
On a Novel Tool Life Relation for Precision Cutting Tools
J. Manuf. Sci. Eng (May,2005)
High Frequency Correction of Dynamometer for Cutting Force Observation in Milling
J. Manuf. Sci. Eng (June,2010)
Synchronous Adjustment of Milling Tool Path Based on the Relative Deviation
J. Manuf. Sci. Eng (October,2011)
Related Chapters
Cutting Performance and Wear Mechanism of Cutting Tool in Milling of High Strength Steel 34CrNiMo6
Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010)
Accuracy of an Axis
Mechanics of Accuracy in Engineering Design of Machines and Robots Volume I: Nominal Functioning and Geometric Accuracy
An Approach to the Tool Wear Model Construction Using Acoustic Signals of Cutting Process
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17