Value of information-based experimental design: Application to process damping in milling

Abstract This paper describes a value of information-based experimental design method that uses Bayesian inference for belief updating. The application is process damping coefficient identification in milling. An analytical process damping algorithm is used to model the prior distribution of the stability boundary (between stable and unstable cutting conditions). The prior distribution is updated using experimental results via Bayesian inference. The updated distribution of the stability boundary is used to determine the posterior process damping coefficient value. A value of information approach for experimental test point selection is then demonstrated which minimizes the number of experiments required to determine the process damping coefficient. Subsequent experimental parameters are selected such that the percent reduction in the standard deviation of the process damping coefficient is maximized. The method is validated by comparing the process damping posterior values to residual sum of squares results using a grid-based experimental design approach. Results show a significant reduction in the number of experiments required for process damping coefficient parameter determination. The advantages of using the value of information approach over the traditional design of experimental methods are discussed.