Prediction of cutting force in milling process using vibration signals of machine tool

This paper presents a method based on the theory of Kalman filter (KF) to predict cutting force through vibration signals in milling process. The acceleration signal and displacement signal of the machine tool spindle vibration are acquired and used for the prediction of the cutting force. In actual machining process, different cutting parameters lead to different time and frequency domain characteristics of cutting force. As system output, the vibration of machine tool is influenced by the input signal of force and machine tool dynamic characteristics. The bandwidth of the transfer function between the cutting force and acceleration should be considered when adopting indirect cutting force measurement method by using vibration sensors. Because of the influence of structural modes of the spindle-tool system, the measurement bandwidth is limited and as a result, it leads to the distortion of measurements containing high frequencies. Consequently, it is necessary to compensate the disturbances caused by structural modes and the bandwidth of the system should be increased properly according to machining conditions. In order to implement the compensating strategy for the measurement, a Kalman filter is designed for the test system. Before the construction of the filter, the frequency response functions (FRFs) of the spindle-tool system are tested through experimental modal analysis (EMA). With the implementation of the compensation, the bandwidth of the testing system is increased significantly. Besides, a strong tracking filter (STF) is designed and applied to the system to improve filter performance. Application of the proposed method has been demonstrated experimentally through cutting tests under different experimental conditions on a three-axis vertical machining center.