Thermal signal-enhanced unscented Kalman filter for tool wear prediction Online publication date: Mon, 02-Dec-2024
by Xianzhe Fu; Zhaoyan Fan; Burak Sencer; Karl Haapala
International Journal of Mechatronics and Manufacturing Systems (IJMMS), Vol. 17, No. 2, 2024
Abstract: Tool wear is a gradual failure of cutting components generally caused by complex cutting conditions during the machining processes. This paper presented a thermal signal-enhanced tool wear prediction method to predict tool wear development during the cutting process. The smart tool samples the multi-modal sensor signals, including tool temperature, vibration, strain, and acoustic emission, which were processed through a neural network model and an unscented Kalman filter (UKF) to predict the tool wear size. Specifically, the measured tool temperature was considered as a tool wear status indicator to update the state transition function in the UKF and enhance the prediction accuracy with the presence of noises in the sensor signals. This method was validated on a commercial CNC lathe. The experimental results showed that the new method achieved an improvement of 8% in tool wear prediction accuracy compared to the conventional UKF.
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