Directionally independent failure prediction of end-milling tools during pocketing maneuvers

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. However, this work proposes to enhance the spectral output of autoregressive models by combining tri-axial accelerometer and tri-axial 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 6 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 towards 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.