Material and Methods: Drilling processes using three brands of drills attached to a robotic arm were compared in terms of thrust force, vibration, noise level, speed deviation, and temperature. A standardised experimental setup was constructed, and measurement data were analysed statistically. Identical artificial bones were drilled 10 times with each drill.
Results: Thrust force measurements, which varied through the cortex and medulla, showed expressive differences for each drill for maximum and mean values (p<0.001). Meaningful differences were obtained for mean vibration values and noise level (p<0.001). Speed variation measurements in drilling showed conspicuous differences with confident statistics (p<0.001). Induced temperature values were measured statistically for Drill 1, Drill 2, and Drill 3 as 78.38±11.49°C, 78.11±7.79°C, and 89.77±7.79°C, respectively.
Conclusion: Thrust force and drill bit temperature were strongly correlated for each drill. Vibration values and noise level, which also had an influential relationship, were in the acceptable range for all experiments. Both thrust force and speed deviation information could be used to detect the drill bit status in the bone while drilling.
METHODS: On the basis of a series of bone milling experiments with commercial artificial bones, an artificial neural network force model is developed to estimate the milling force of different bone densities as a function of the milling feed rate and spindle speed. The model estimations are used to identify the bone density at the cutting zone by comparing the actual milling force with the estimated one.
RESULTS: The verification experiments indicate the ability of the proposed method to distinguish between one cortical and two cancellous bone densities.
CONCLUSIONS: The significance of the proposed method is that it can be used to discriminate a set of different bone density layers for a range of the milling feed rate and spindle speed.