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Modeling and analysis of tool wear prediction based on SVD and BiLSTM


Wear is one of the main forms of tool failure during machining. The prediction of tool wear is of great significance for ensuring the high quality of the workpiece. In order to improve prediction accuracy of tool wear, a tool wear prediction model based on singular value decomposition (SVD) and bidirectional long short-term memory neural network (BiLSTM) is proposed. The cutting force signal is taken as the monitoring signal. Firstly, the raw cutting force signal is reconstructed by Hankle matrix, and the SVD of the reconstructed matrix is performed to extract the signal features. Then, SVD features of the current sampling period and the previous four sampling periods are taken as the input, and the tool wear prediction value at the current time is obtained based on the BiLSTM. The experimental results show that the proposed SVD-BiLSTM model can effectively predict the tool wear and obtain higher prediction accuracy than other comparison models.

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  1. Guo JC, Li AH (2019) Advances in monitoring technology of tool wear condition [J]. Tool Engineering 53(05):3–13

    Google Scholar 

  2. Patra K, Jha AK, Szalay T et al (2017) Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals [J]. Precis Eng 48:279–291

    Article  Google Scholar 

  3. Li XL, Li HX, Guan XP (2004) Fuzzy estimation of feed-cutting force from current measurement - a case study on intelligent tool wear condition monitoring [J]. IEEE Trans Syst Man Cybern Part C Appl Rev 34(4):506–512

    Article  Google Scholar 

  4. Li X, Dong S, Venuvinod PK (2000) Hybrid learning for tool wear monitoring [J]. Int J Adv Manuf Technol 16(5):303–307

    Article  Google Scholar 

  5. Dimla DE (2002) The correlation of vibration signal features to cutting tool wear in a metal turning operation[J]. Int J Adv Manuf Technol 19(10):705–713

    Article  Google Scholar 

  6. Wu D, Huang M (2014) Application of vibration signal monitoring in tool wear fault diagnosis [J]. Mechanical Engineering & Automation (02):121–122+125

  7. Pai PS, Rao PKR (2002) Acoustic emission analysis for tool wear monitoring in face milling[J]. Int J Prod Res 40(5):1081–1093

    Article  Google Scholar 

  8. Zhou JH, Pang CK, Zhong ZW et al (2011) Tool wear monitoring using acoustic emissions by dominant-feature identification[J]. IEEE Trans Instrum Meas 60(2):547–559

    Article  Google Scholar 

  9. Li X, Tso SK (1999) Drill wear monitoring based on current signals [J]. Wear 231(2):172–178

    Article  Google Scholar 

  10. Li X, Djordjevich A, Venuvinod PK (2000) Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring[J]. IEEE Trans Ind Electron 47(3):697–702

    Article  Google Scholar 

  11. Chen Y, Jin Y, Jiri G (2018) Predicting tool wear with multi-sensor data using deep belief networks[J]. Int J Adv Manuf Technol

  12. Li XL, Yuan ZJ (1998) Tool wear monitoring with wavelet packet transform-fuzzy clustering method[J]. Wear 219(2):145-154

    Article  Google Scholar 

  13. Wang G, Zhang Y, Liu C et al (2016) A new tool wear monitoring method based on multi-scale PCA [J]. J Intell Manuf:1–10

  14. Babouri MK, Ouelaa N, Djamaa MC et al (2017) Prediction of tool wear in the turning process using the spectral center of gravity [J]. J Fail Anal Prev:1–9

  15. Li N, Chen Y, Kong D et al (2017) Force-based tool condition monitoring for turning process using v-support vector regression [J]. Int J Adv Manuf Technol 91(1–4):351–361

    Article  Google Scholar 

  16. Li G, Du X, Zhao LL et al (2019) Design of milling-tool wear monitoring system based on EEMD-SVM [J]. Automation & Instrumentation 06:30–32

    Google Scholar 

  17. Rui Z, Ruqiang Y, Zhenghua C et al (2019) Deep learning and its applications to machine health monitoring[J]. Mech Syst Signal Process 115:213–237

    Article  Google Scholar 

  18. Zhao R, Wang J, Yan R et al (2016) Machine health monitoring with LSTM networks[C]// International Conference on Sensing Technology. IEEE

  19. Malhotra P , Ramakrishnan A , Anand G , et al. LSTM-based encoder-decoder for multi-sensor anomaly detection[J]. 2016

  20. Bruin TD, Verbert K, Babuška R (2017) Railway track circuit fault diagnosis using recurrent neural networks. IEEE Trans Neural Netw Learn Syst 28:523–533.

    MathSciNet  Article  Google Scholar 

  21. Graves A, Fernández S, Schmidhuber J, Bidirectional LSTM (2005) Networks for improved phoneme classification and recognition[M]. Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005

  22. Zhao XZ, Bang-Yan YE, Lin Y (2011) Amplitude modulation feature extraction of bearing vibration signal using singular value decomposition[J]. Transactions of Beijing Institute of Technology 31(5):572–577

    Google Scholar 

  23. Huang J N, Wang S H, Ma C. Fault diagnosis of rolling bearing based on SVD-EEMD and BP neural network [J]. J Beijing Inf Sci Technol University,2019,34(02):69–74

  24. Li X, Lim B, Zhou J, Huang S, Phua S, Shaw K, Er M (September 2009) Fuzzy neural network modelling for tool wear estimation in dry milling operation. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, San Diego, CA, USA, pp 27–30

    Google Scholar 

  25. Kingma D, Ba J (2014) Adam: a method for stochastic optimization[J]. Comput Sci

Download references


This work was supported by the National Natural Science Foundation of China (Grant No. 51775374), Inner Mongolia Autonomous Region of Science and technology innovation guiding project KCBJ2018028, College Scientific Research Project of Inner Mongolia Autonomous Region NJZY18159, Natural Science Foundation of the Inner Mongolia Autonomous Region of China 2018MS05025, Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region NJYT-19-B15.

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Correspondence to Xiaoqiang Wu.

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Wu, X., Li, J., Jin, Y. et al. Modeling and analysis of tool wear prediction based on SVD and BiLSTM. Int J Adv Manuf Technol 106, 4391–4399 (2020).

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  • Tool wear prediction
  • SVD
  • RNN
  • BiLSTM