Tool wear is an important limitation to machining productivity. In this paper, remaining useful tool life predictions using the random walk method of Bayesian inference is demonstrated. End milling tests were performed on a titanium workpiece and spindle power was recorded. The power root mean square value in the time domain was found to be sensitive to tool wear and was used for tool life predictions. Sample power root mean square growth curves were generated and the probability of each curve being the true growth curve was updated using Bayes’ rule. The updated probabilities were used to determine the remaining useful tool life. Results show good agreement between the predicted tool life and the true remaining life. The proposed method takes into account the uncertainty in tool life and the percentage of nominal power root mean square value at the end of tool life.

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