Ensemble Kalman filtering for force model identification in milling

Abstract

Abstract Mechanistic force models are popular to describe the force in cutting technology. Process simulation, process optimization, and process control rely on the accuracy of these models. Standard identification techniques are not capable of identifying a mechanistic force model on-line and in hard real-time. However, it is necessary to adjust the model to increasing tool wear, e.g. in a model predictive controller for force control in milling. This work introduces the ensemble Kalman filter to the field of force model identification in cutting technology - enabling for the first time a continuous parametrization of mechanistic force models. The approach shows high accuracy and fast convergence in spite of the presence of measurement noise. The novel approach is validated statistically using 1000 random initial distributions and different ensemble sizes. The ideal, simulated force signals is augmented with different levels of noise (signal-to-noise ratios of 50, 15, five). Nevertheless, the filter converges within three, eight, and 32 revolutions of the tool respectively.