A Milling Cutter Wear Monitoring Method Based on ABiGRU
LIU Chao1,WANG Chen1,2,ZHANG Xiu-feng1,LU Xu-xiang1
1. Hubei University of Automotive Technology, Shiyan,Hubei, 442002, China
2. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai 200072, China
Abstract In order to accurately monitor tool wear in the milling process, a tool wear monitoring model based on attention and bidirectional gated recurrent unit (ABiGRU) was proposed. In the monitoring model, firstly, the time series data collected by vibration, force, and acoustic emission sensors were analyzed in the time domain, frequency domain, and time-frequency domain, and spearman coefficient was used to extract 20-dimensional features strongly correlated with the average rear tool surface wear. Secondly, the ELU activation function was introduced to optimize the BiGRU network and solve the gradient disappearance problem, in the meantime, the internal attention mechanism was used to improve the models ability to capture important feature information and quickly realize the mapping from feature to tool wear value. Finally, compared with RNN, LSTM, GRU, BiLSTM, and BiGRU, the results shown that the model could not only accurately characterize the tool wear degree, but also the accuracy and efficiency is improved greatly.
Agogino A, Goebel K. Mill Data Set［EB/OL］. http: //tiarc. nasa. gov/project/prognostic-data-repository.
Chen J, Cai K Q, Tao S Y, et al. Fault Diagnosis Method of Rolling Bearing Based on IITD Fuzzy Entropy and Random Forest［J］. Acta Metrologica Sinica, 2021, 42(6): 774-779.
［3］
Yu J, Liang S, Tang D, et al. A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction［J］. The International Journal of Advanced Manufacturing Technology, 2017, 91(1): 201-211.
Chen F, Dong J Y. Nano-machining AFM Tip Wear Monitoring Based on Time Series Data and Support Vector Machine［J］. Acta Metrologica Sinica, 2019, 40(4): 647-654.
Liu H, Liu Z Y, Jia W Q, et al. Current research and challenges of deep learning for equipment remaining useful life prediction［J］. Computer Integrated Manufacturing System, 2021, 27(1): 34-52.
He Z J, Zhou Z X. Tool Wear State Monitoring Based on Variational Mode Decomposition and Correlation Dimension and Relevance Vector Machine［J］. Acta Metrologica Sinica, 2018, 39(2): 182-186.
［6］
Schmidhuber J. A local learning algorithm for dynamic feedforward and recurrent networks［J］. Connection Science, 1989, 1(4): 403-412.
Li F, Chen Y, Wang J X, et al. State trend prediction of rolling bearing based on reinforcement learning unit matching recurrent neural network［J］. Computer Integrated Manufacturing System, 2020, 26(8): 2050-2059.
Zhong B H, Wang L, Zhong S S. Selective Assembly for Coordinator Parts by RNGRU Based on Comprehensive Grey Relational Order Model［J］. Chinese Journal of Mechanical Engineering, 2021, 32(3): 314-320,356.
He Y, Lin J J, Wang Y L, et al. In-process Tool Wear Monitoring Model Based on LSTM-CNN［J］. Chinese Journal of Mechanical Engineering, 2020, 31(16): 1959-1967.
Wu F H, Zhong H, Zhang Q, et al. Tool Wear Condition Monitoring Based on Convolution-gated Recurrent Neural Network［J］. Acta Metrologica Sinica, 2021, 42(8): 1034-1040.
Wang J C, Bao J S, Liu T Y, et al. Online method for worker operation recognition based on attention of workpiece［J］. Computer Integrated Manufacturing System, 2021, 27(4): 1099-1107.
Zheng S, Ristovski K, Farahat A, et al. Long short-term memory network for remaining useful life estimation［C］//2017 IEEE international conference on prognostics and health management (ICPHM). IEEE, 2017: 88-95.
［11］
Wang J, Yan J, Li C, et al. Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction［J］. Computers in Industry, 2019, 111: 1-14.
Wang J J, Zhou Y Q, Hao Z. Multi-sensor Human Activity Recognition Based on Attention Model［J］. Acta Metrologica Sinica, 2019, 40(6): 958-969.
Guo B S, Han T J, Zhang Y, et al. Tool Wear Monitoring Method Based on One-Dimensional Residual Network with Threshold Module［J］. Acta Metrologica Sinica, 2022, 43(4): 501-506.
2023, Vol. 44 
