Milling Cutter Wear Prediction Based on Bidirectional Long Short-Term Memory Neural Networks

Conference: ISMSEE 2022 - The 2nd International Symposium on Mechanical Systems and Electronic Engineering
02/25/2022 - 02/27/2022 at Zhuhai, China

Proceedings: ISMSEE 2022

Pages: 6Language: englishTyp: PDF

Authors:
Zhou, Chengpeng; Wang, Weijun (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China & University of Chinese Academy of Sciences, Beijing, China)
Hou, Zhicheng (Guangdong Polytechnic Normal University, School of Automation, Guangzhou, China)
Feng, Wei (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China)

Abstract:
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 cutters based on bidirectional long short-term memory neural networks and attention mechanism. 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 bidirectional long short-term memory neural networks to learn the complex mapping relationship between the extracted features and the wear amount. Experimental results show that this method reduces mean square error by 70.81% and 47.97% compared to the SVR model and the BPNN model respectively, and reduces mean square error by 79.69% and mean absolute percentage error by 5.977% compared to the CNN, enables a more accurate and reliable milling cutter wear prediction.