Estimating Milling Forces From Vibration Measurements

Abstract

Abstract Machining force and vibration signals are commonly used for process monitoring. While low-cost accelerometers are conveniently installed in machining setups, the direct measurement of machining forces in industrial applications is challenging. As an alternative to direct measurement, cutting forces can be estimated indirectly from vibration measurements, enabling the simultaneous monitoring of vibrations and forces from vibration signals only. In this paper, we two methods for estimating the dynamic milling forces from acceleration measurements during milling processes. The first method applies offline regularized deconvolution to the measured acceleration data to extract the forces causing them. The second method designs an online Augmented Kalman Filter to observe the forces as the augmented system states. The efficiency and performance of both methods are studied experimentally. The comparison between the indirectly estimated forces and the directly measured ones confirms the feasibility of using acceleration sensors to monitor the machining forces and the resulting vibrations simultaneously. Nevertheless, because the low-frequency contents of the forces are filtered in the resulting accelerations, only the dynamic component of the forces can be recovered. Experimental comparison of regularized deconvolution and augmented Kalman filter methods shows that the latter is more effective in recovering a larger portion of low-frequency content of the forces. Despite missing the low-frequency content, the reconstructed dynamic forces can still be used for process monitoring in applications where force sensors cannot be installed.