Probabilistic modeling and prediction of a milling tool life and reliability using bayesian statistics

Abstract

This paper represents probabilistic modeling and prediction for lifetime and reliability of a milling tool using Bayesian statistics. Markov Chain Monte Carlo (MCMC) simulation is applied to Taylor tool life model to develop probabilistic models. Prior probabilities are established from literature review and posterior probabilities of the Taylor model parameters are obtained using milling experiments. The experiments were conducted to train and test the probabilistic models under a range of cutting speeds, 300–400 m/min. In this regard, the training datasets are used to update the tool life model and the test datasets are used to validate the probabilistic tool life model. Posterior distributions of the tool life are used to perform reliability analysis using reliability functions. The probabilistic models investigate the effect of cutting speeds on tool life probability distributions and the reliability functions. It is shown that the tool life posterior and reliability functions predict the measured tool life values within the uncertainty intervals and maximum prediction error of 18%, using only two training data points.