Skip to main content
SpringerLink
Log in

A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Tool condition monitoring (TCM) during the manufacturing process is of great significance for ensuring product quality and plays an important role in intelligent manufacturing. Current TCM systems deployed in the local device or cloud computing environment unable meet the requirements of low response latency and high accuracy at the same time. The emerging fog computing provides new solutions for the above problem. This paper presents a tool wear monitoring and prediction (TWMP) system based on deep learning models and fog computing. In order to improve monitoring and prediction accuracy, we propose a multiscale convolutional long short-term memory model (MCLSTM) to complete the tool wear monitoring task and a bi-directional LSTM model (BiLSTM) to complete the tool wear prediction task. To reduce the response latency of the TWMP system, we deploy the MCLSTM model and the BiLSTM model in a fog computing architecture. The fog computing architecture consists of an edge computing layer, a fog computing layer, and a cloud computing layer. The edge computing layer undertakes real-time signal collection task. The fog computing layer undertakes real-time tool wear monitoring task. The cloud computing layer with powerful computing resources undertakes intensive computing and latency-insensitive tasks such as data storage, tool wear prediction, and model training. A twist drill wear monitoring and prediction experiment is conducted to test the performance of the proposed system in terms of accuracy, response time, and network bandwidth consumption.

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

Similar content being viewed by others

Abbreviations

IIOT:

Industry internet of things

TWMP:

Tool wear monitoring and prediction

TCM:

Tool condition monitoring

LSTM:

Long short-term memory

CNN:

Convolutional neural network

MCLSTM:

Multiscale convolutional long short-term memory

BiLSTM:

Bi-directional long short-term memory

DBN:

Deep belief network

SAE:

Sparse autoencoders

CBLSTM:

Convolutional bi-directional long short-term memory

MCNN:

Multiscale convolutional neural network

MLSTM:

Multiscale long short-term memory

FC:

Fully connected

BN:

Batch normalization

MAPE:

Mean absolute percentage error

RMSE:

Root mean squared error

GRU:

Gated recurrent unit

BiGRU:

Bi-directional gated recurrent unit

References

  1. Fatemeh A, Antoine T, Marc T (2018) Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process. Int J Adv Manuf Technol 98:3217–3227. https://doi.org/10.1109/ICSensT.2016.7796266

    Article  Google Scholar 

  2. Lei Y, Jia F, Lin J, Xing SB, Ding SX (2016) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 63(5):3137–3147. https://doi.org/10.1109/TIE.2016.2519325

    Article  Google Scholar 

  3. Scheffer C, Engelbrecht H, Heyns PS (2005) A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear. Neural Comput Applic 14(4):325–336. https://doi.org/10.1007/s00521-005-0469-9

    Article  Google Scholar 

  4. Yu J, Liang S, Tang D, Liu H (2017) A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. Int J Adv Manuf Technol 91(1-4):201–211. https://doi.org/10.1007/s00170-016-9711-0

    Article  Google Scholar 

  5. Lu Z, Wang M, Dai W (2019) In-process complex machining condition monitoring based on deep forest and process information fusion. Int J Adv Manuf Technol 104(7-8):1953–1966. https://doi.org/10.1007/s00170-019-03919-4

    Article  Google Scholar 

  6. Li N, Chen Y, Kong D, Tan S (2017) Force-based tool condition monitoring for turning process using v-support vector regression. Int J Adv Manuf Technol 91(1-4):351–361. https://doi.org/10.1007/s00170-016-9735-5

    Article  Google Scholar 

  7. Cuka B, Kim DW (2017) Fuzzy logic based tool condition monitoring for end-milling. Robot Comput Integr Manuf 47:22–36. https://doi.org/10.1016/j.rcim.2016.12.009

    Article  Google Scholar 

  8. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647

    Article  MathSciNet  MATH  Google Scholar 

  9. Bengio Y, Delalleau O (2011) On the expressive power of deep architectures. Proceedings of the 14th International Conference on Discovery Science 2011: 18-36

  10. Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. Neural Comput. Proceedings of the 1999 the 9th International Conference on Artificial Neural Networks 1999: 850-855. https://doi.org/10.1049/cp:19991218

  11. Qiao H, Wang T, Wang P, Zhang L, Xu M (2019) An adaptive weighted multiscale convolutional neural network for rotating machinery fault diagnosis under variable operating conditions. IEEE Access 7:118954–118964. https://doi.org/10.1109/ACCESS.2019.2936625

    Article  Google Scholar 

  12. Teerapittayanon S, McDanel B, Kung HT (2017) Distributed deep neural networks over the cloud, the edge and end devices. Proceedings of the 37th IEEE International Conference on Distributed Computing Systems 2017: 328-339. https://doi.org/10.1109/ICDCS.2017.226

  13. Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M, Helu M (2015) Cloud-enabled prognosis for manufacturing. CIRP Ann 64(2):749–772. https://doi.org/10.1016/j.cirp.2015.05.011

    Article  Google Scholar 

  14. Wu D, Jennings C, Terpenny J, Kumara S, Gao RX (2018) Cloud-based parallel machine learning for tool wear prediction. J Manuf Sci Eng, Trans ASME 140(4):041005–041015. https://doi.org/10.1115/1.4038002

    Article  Google Scholar 

  15. Caggiano A (2018) Cloud-based manufacturing process monitoring for smart diagnosis services. Int J Comput Integr Manuf 31(7):612–623. https://doi.org/10.1080/0951192X.2018.1425552

    Article  Google Scholar 

  16. Bonomi F, Milito R, Zhu J (2012) Fog computing and its role in the internet of things. Proc First Ed MCC Workshop Mobile Cloud Comput 2012:13–16. https://doi.org/10.1145/2342509.2342513

    Article  Google Scholar 

  17. Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. Big Data Internet Things: Roadmap Smart Environ 2014:169–186. https://doi.org/10.1007/978-3-319-05029-4_7

    Article  Google Scholar 

  18. Chen Y, Jin Y, Jiri G (2018) Predicting tool wear with multi-sensor data using deep belief networks. Int J Adv Manuf Technol 99(5-8):1917–1926. https://doi.org/10.1007/s00170-018-2571-z

    Article  Google Scholar 

  19. Shi C, Panoutsos G, Luo B, Liu H, Li B, Lin X (2019) Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultraprecision manufacturing. IEEE Trans Ind Electron 66(5):3794–3803. https://doi.org/10.1109/TIE.2018.2856193

    Article  Google Scholar 

  20. Zhao R, Wang J, Yan R, Mao K (2016) Machine health monitoring with LSTM networks. Proc Int Conf Sens Technol 2016:1–6. https://doi.org/10.1109/ICSensT.2016.7796266

    Article  Google Scholar 

  21. Wang J, Zhao R, Wang D, Yan R, Mao K, Shen F (2017) Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans Ind Electron 65(2):1539–1548. https://doi.org/10.1109/ICSensT.2016.7796266

    Article  Google Scholar 

  22. Tao Z, An Q, Liu G, Chen M (2019) A novel method for tool condition monitoring based on long short-term memory and hidden Markov model hybrid framework in high-speed milling Ti-6Al-4V. Int J Adv Manuf Technol 105(7-8):3165–3182. https://doi.org/10.1007/s00170-019-04464-w

    Article  Google Scholar 

  23. Cao X, Chen B, Yao B, He W (2019) Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification. Comput Ind 106:71–84. https://doi.org/10.1016/j.compind.2018.12.018

    Article  Google Scholar 

  24. 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. https://doi.org/10.3390/s18092932

    Article  Google Scholar 

  25. Zhao R, Yan R, Wang J, Mao K (2017) Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors 17(2):273. https://doi.org/10.3390/s17020273

    Article  Google Scholar 

  26. Wu D, Liu S, Zhang L, Terpenny J, Gao R, Kurfess T, Guzzo JA (2017) A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. J Manuf Syst 43:25–34. https://doi.org/10.1016/j.jmsy.2017.02.011

    Article  Google Scholar 

  27. Li L, Ota K, Dong M (2018) Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Transact Industrial Inform 14(10):4665–4673. https://doi.org/10.1109/TII.2018.2842821

    Article  Google Scholar 

  28. O’Donovan P, Gallagher C, Leahy K, O’Sullivan DTJ (2019) A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications. Comput Ind 110:12–35. https://doi.org/10.1109/TII.2018.2842821

    Article  Google Scholar 

  29. Jiang G, He H, Yan J, Xie P (2018) Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Trans Ind Electron 66(4):3196–3207. https://doi.org/10.1109/TIE.2018.2844805

    Article  Google Scholar 

  30. Glorot X, Bordes A, Bengio YS (2011) Deep sparse rectifier neural networks. Proceedings of the 14th international conference on artificial intelligence and statistics (AISTATS). J Mach Learn Res 2011:315–323

    Google Scholar 

  31. Loffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning 2015: 448-456

  32. Matthew DZ (2012) Adadelta: an adaptive learning rate method. arXiv preprint. Computer Science, arXiv:1212.5701

  33. Harun MHS, Ghazali MF, Yusoff AR (2017) Analysis of tri-axial force and vibration sensors for detection of failure criterion in deep twist drilling process. Int J Adv Manuf Technol 89(9–12):3535–3545

    Article  Google Scholar 

Download references

Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 51975402).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Tianjin University, Tianjin, 300354, China

    Huihui Qiao, Taiyong Wang & Peng Wang

  2. Renai College, Tianjin University, Tianjin, 301636, China

    Taiyong Wang

Authors
  1. Huihui Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Taiyong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Taiyong Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Qiao, H., Wang, T. & Wang, P. A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing. Int J Adv Manuf Technol 108, 2367–2384 (2020). https://doi.org/10.1007/s00170-020-05548-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05548-8

Keywords

Search

Navigation