Skip to main content
SpringerLink
Log in

Tool wear prediction using convolutional bidirectional LSTM networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Machine health monitoring systems are vital components of modern manufacturing industries. As advanced sensors collecting machine health-related data become commonplace, such systems have started adopting data-driven approaches to harness the collected data. However, dealing with noisy data and gleaning the spatial and temporal correlation within the data is a challenge. Extant literature focuses on the use of feature extraction to judge the state of normal and worn tools. However, the process of detecting the features of worn tools can often damage the workpiece material and result in machine downtime, which increases costs. Using recent developments in artificial intelligence, data features can be used to gauge the condition of tools in real-time. This paper proposes Holistic–Local Long Short-Term Memory (HLLSTM), a deep learning approach that adopts Long Short-Term Memory to predict tool wear based on holistic and local features. The data is divided into segments to learn short-term data features and further divided into holistic training and local training data to extract more implicit feature information to improve the accuracy of tool wear prediction. Experimental results indicate that HLLSTM can reduce the mean absolute error of real tool wear value two-fold and accurately predict tool wear.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Lasi H, Fettke P, Kemper HG, Feld T, Hoffmann M (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242

    Article  Google Scholar 

  2. Lu Y, Xu X, Xu J (2014) Development of a hybrid manufacturing cloud. J Manufact Syst 33(4):551–566

    Article  MathSciNet  Google Scholar 

  3. Zhao R, Wang D, Yan R, Mao K, Shen F, Wang J (2017) Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans Ind Electron 65(2):1539–1548

    Article  Google Scholar 

  4. Qiao H, Wang T, Wang P, Qiao S, Zhang L (2018) A time-distributed spatiotemporal feature learning method for machine health monitoring with multi-sensor time series. Sensors 18(9):2932

    Article  Google Scholar 

  5. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237

    Article  Google Scholar 

  6. Xu Y, Sun Y, Liu X, Zheng Y (2019) A digital-twin-assisted fault diagnosis using deep transfer learning. IEEE Access 7:19990–19999

    Article  Google Scholar 

  7. Serin G, Sener B, Ozbayoglu AM, Unver HO (2020) Review of tool condition monitoring in machining and opportunities for deep learning. Int J Adv Manufact Technol. https://doi.org/10.1007/s00170-020-05449-w

    Article  Google Scholar 

  8. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 770–778

  9. Zhang HB, Zhang YX, Zhong B, Lei Q, Yang L, Du JX, Chen DS (2019) A comprehensive survey of vision-based human action recognition methods. Sensors 19(5):1005

    Article  Google Scholar 

  10. Toshev A, Szegedy C (2014) Deeppose: Human pose estimation via deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1653–1660

  11. Cao Z, Hidalgo G, Simon T, Wei SE, Sheikh Y (2018) OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. arXiv preprint arXiv:1812.08008

  12. Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: Methods and applications. J Manufact Syst 48:144–156

    Article  Google Scholar 

  13. Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manufact Syst 48:157–169

    Article  Google Scholar 

  14. Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Article  Google Scholar 

  15. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Article  Google Scholar 

  16. Wang S, Cao J, Yu P (2020) Deep learning for spatio-temporal data mining: A survey. IEEE Transactions on Knowledge and Data Engineering

  17. Cheng Y, Zhu H, Wu J, Shao X (2018) Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Trans Ind Inf 15(2):987–997

    Article  Google Scholar 

  18. Essien A, Giannetti C (2020) A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders. IEEE Trans Ind Inf 16(9):6069–6078

    Article  Google Scholar 

  19. Liu R, Meng G, Yang B, Sun C, Chen X (2016) Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine. IEEE Trans Ind Inf 13(3):1310–1320

    Article  Google Scholar 

  20. Lei Y, Han D, Lin J, He Z (2013) Planetary gearbox fault diagnosis using an adaptive stochastic resonance method. Mech Syst Signal Process 38(1):113–124

    Article  Google Scholar 

  21. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 1:1097–1105

    Google Scholar 

  22. He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition(CVPR). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June, 2016

  23. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  24. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural Architectures for Named Entity Recognition. In Proceedings of NAACL-HLT, June 2016

  25. Mousa AED, Schuller BW (2016) Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks for Grapheme-to-Phoneme Conversion Utilizing Complex Many-to-Many Alignments. Interspeech 2836–2840

  26. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

    Article  MathSciNet  Google Scholar 

  27. Ray A, Rajeswar S, Chaudhury S (2015) Text recognition using deep BLSTM networks. In Proc. of 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1-6

  28. Zhao R, Yan R, Wang J, Mao K (2017) Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors 17(2):273

    Article  Google Scholar 

  29. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  30. 2010 phm Society Conference Data Challenge(2010). https://phmsociety.org/competition/phm/10

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, Providence University, Taiwan, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung, Taiwan

    Yu-Wei Chan

  2. College of Computing and Informatics, Providence University, Taiwan, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung, Taiwan

    Tsan-Ching Kang

  3. Department of Computer Science, Research Center for Smart Sustainable Circular Economy, Tunghai University, Taiwan, No.1727, Sec.4, Taiwan Boulevard, Xitun District, Taichung, Taiwan

    Chao-Tung Yang

  4. Department of Computer Science and Communication Engineering, Providence University, Taiwan, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung, Taiwan

    Chih-Hung Chang & Yin-Te Tsai

  5. Department of Computer Science and Information Engineering, Providence University, Taiwan, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung, Taiwan

    Shih-Meng Huang

Authors
  1. Yu-Wei Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tsan-Ching Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chao-Tung Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chih-Hung Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shih-Meng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yin-Te Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chih-Hung Chang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chan, YW., Kang, TC., Yang, CT. et al. Tool wear prediction using convolutional bidirectional LSTM networks. J Supercomput 78, 810–832 (2022). https://doi.org/10.1007/s11227-021-03903-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03903-4

Keywords

Search

Navigation