Continuous identification for mechanistic force models in milling

Abstract

Force determines the product quality, the productivity and the safety of a milling process. Mechanistic force models are the key to understand, optimize or control the cutting process. They combine the undeformed chip parameter with empiric tuning coefficients in a gray-box model. Identifying those coefficients is costly in both, time and number of experiments. This paper introduces two recursive identification methods for force model identification: recursive least squares and ensemble Kalman filters. The model is nonlinear. The ensemble Kalman filter shows an extraordinary robustness against measurement noise and a fast convergence time -depending on the selection of the ensemble size and the measurement noise. The recursive least squares fit serves as a benchmark but is highly sensitive to measurement noise. It is the first time that a continuous identification is examined for mechanistic force models in milling.