Bayesian approach to determine force model parameters for the prediction of cutting forces in turning operations

Abstract

The prediction of cutting forces is a major topic in predesigning turning operations. An efficient method to predict cutting forces are machining simulations taking mechanistic force models into account. However, their accuracy highly depends on the model parameters, whose calibration requires numerous time- and cost-consuming experiments. In order to calibrate model parameters with low experimental effort, this study presents a Bayesian Makrov Chain Monte Carlo (MCMC) method. Force model parameters are conditioned to only few measurements of turning experiments and lead to accurate results of machining simulations. Due to low experimental effort, this approach is of major interest for industrial applications.