Precompensation of machine dynamics for cutting force estimation based on disturbance observer

Complex machine-structure dynamics of a movable stage affects observer-based cutting force estimation. A dynamic compensation approach based on the concept of machine-in-the-loop learning is proposed to enhance the accuracy of cutting force estimation based on a disturbance-observer. Machine dynamics induced estimation errors are pre-compensated by modifying a digital filter representing an inverse disturbance transfer function. The order and parameters of the filter are self-optimized to enhance the estimation accuracy during iterative pre-milling tests with various rotational spindle speeds. The experimental results show that the proposed self-optimized filter achieves accurate wide-band cutting force estimation in milling process.