Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks - ZHOU Chengpeng; WANG Weijun; HOU Zhicheng; FENG Wei (Kongzhi Yu Xinxi Jishu)
The wear of milling cutters during high-speed milling processes affects the quality of the work-piece, productivity and manufacturing costs. Accurate tool wear prediction can therefore optimize production decisions and avoid losses due to tool wear. To improve the prediction precision, this paper proposes a method for predicting the wear of milling tools based on feature extraction and long short-term memory neural networks. By extracting the time domain features, frequency domain features and time-frequency domain features based on time series data generated during industrial processes, and using long short-term memory neural networks to learn the complex mapping relationship between the extracted features and the wear amount, it infer the future wear of the milling cutter. Experimental results show that this method reduces mean square error by 67.07% and 41.31% compared with the SVR model and the BPNN model respectively, and reduces mean square error by 77.09% and mean absolute percentage error by 5.685% compared with the CNN, enables a more accurate and reliable milling cutter wear prediction.