Intelligent monitoring of milling tool wear based on milling force coefficients by prediction of instantaneous milling forces

Abstract

The spindle current of a machine tool contains a wealth of machining information and by analyzing its signal characteristics wear monitoring of milling tools can be achieved. Most current research in tool wear monitoring has been achieved by intelligent classification methods, but such methods are unable to identify wear states when cutting parameters change. To address this challenge, the paper proposes a new tool wear monitoring framework that identifies the wear state of milling cutters under different cutting parameters. The first step is to establish the mapping relationship between the spindle current signal and the milling force signal through neural networks under a variety of cutting parameters. Based on the mapping property between two signals, a TCN-LSTM based neural network prediction model is proposed to achieve accurate prediction of milling forces by spindle current signal. Various cutting information is then calculated based on the predicted milling forces and PLC communication, and a method is proposed to identify the machining status of the tool: the milling force coefficient monitoring method. The method reflect the wear status of the milling cutter by monitoring the range of threshold values, making the identification of the tool wear state without interference from variable spindle speeds, feeds and depths of cut. The proposed method provides a new solution for tool wear monitoring, breaking the inherent thinking of intelligent tool wear classification, and the experimental results prove the effectiveness and generality of the proposed method.