Bayesian uncertainty quantification and propagation for prediction of milling stability lobe

Abstract To predict the stability boundary of cutting operation, it is necessary to establish process force model and dynamic mechanical behavior model. Uncertainty of model parameters may lead to significant differences between predicted and measured stability boundary. In this paper, a novel method for determining boundary of stability in milling, which performs the stability analysis on an uncertain dynamic milling model, is presented. Firstly, the input parameters of milling stability prediction model are considered as random variables. Subsequently, sensitivity analysis is used to determine the most influential parameters, the posterior distributions of all influential parameters are estimated using a Bayesian inference framework, which is built based on an improved Ensemble Markov Chain Monte Carlo (IGWMCMC) algorithm. Compared with the traditional method, IGWMCMC is not affected by affine transformations of space, which can achieve much better convergence on badly scaled problems and the computational costs are cheaper. Finally, the posterior distributions of uncertainties calculated using Bayesian inference are propagated through the computational model. The experimental results verify the reliability of the calculated boundaries of stability lobe diagrams.