Fluid Lubrication and Cooling Effects in Diamond Grinding of Human Iliac Bone

Background and Objectives: Although there have been research on bone cutting, there have been few research on bone grinding. This study reports the measurement results of the experimental system that simulated partial laminectomy in microscopic spine surgery. The purpose of this study was to examine the fluid lubrication and cooling in bone grinding, histological characteristics of workpieces, and differences in grinding between manual and milling machines. Materials and Methods: Thiel-fixed human iliac bones were used as workpieces. A neurosurgical microdrill was used as a drill system. The workpieces were fixed to a 4-component piezo-electric dynamometer and fixtures, which was used to measure the triaxial power during bone grinding. Grinding tasks were performed by manual activity and a small milling machine with or without water. Results: In bone grinding with 4-mm diameter diamond burs and water, reduction in the number of sudden increases in grinding resistance and cooling effect of over 100 °C were confirmed. Conclusion: Manual grinding may enable the control of the grinding speed and cutting depth while giving top priority to uniform torque on the work piece applied by tools. Observing the drill tip using a triaxial dynamometer in the quantification of surgery may provide useful data for the development of safety mechanisms to prevent a sudden deviation of the drill tip.

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. 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.