Skip to main content
SpringerLink
Log in

A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring

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

Abstract

With the development of Industry 4.0 technology including the Internet of Things (IoT) and deep learning techniques, it is important to reduce maintenance costs and ensure the safety of manufacturing process. The cutting tool degradation can cause significant economic losses and risks for machine users. Accurate prediction of cutting tool is important for making full use of cutter life. Deep learning plays an important role for tool condition monitoring. To overcome these difficulties, this paper aims to propose a new approach in the application of deep learning to estimate the tool wear during the milling process. The proposed methodology is based on the data-driven approach using Variational Mode Decomposition (VMD) and deep learning. Two deep learning machines used in this study, Convolutional Neural Networks (CNN) and Bidirectional long short-term memory (BiLSTM) to achieve collaborative data mining on (VMD) and to enhance the accuracy of modeling. VMD is a new decomposition technique used to decompose signal into sub-time series called intrinsic mode functions (IMFs). However, the VMD performances specifically depend on the constraints parameters that need to be pre-determined for VMD method especially the number of modes. The model development based on 1D-CNN and BiLSTM are selected by using the IMFs as inputs. The performance of the proposed approach is further improved by using the combined CNN and BiLSTM and has shown higher performances in prediction, compared to traditional learning techniques and adopted in previous works highlight the proposed prognostics method’s superiority. Among all models, the VMD-CNN-BiLSTM achieve the best performance of modeling with respect to efficiency and effectiveness.

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
Fig. 19

Similar content being viewed by others

Data Availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request. [28, 29].

References

  1. Mohanraj T, Shankar S, Rajasekar R, Sakthivel N, Pramanik A (2020) Tool condition monitoring techniques in milling processa review. J Mater Res Technol 9(1):1032–1042

    Google Scholar 

  2. Liu W, Kong C, Niu Q, Jiang J, Zhou X (2020) A method of nc machine tools intelligent monitoring system in smart factories. Robot Comput Integr Manuf 61:101842

    Google Scholar 

  3. Soualhi M, Nguyen KT, Medjaher K (2020) Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mech Syst Signal Process 142:106680

    Google Scholar 

  4. Ahmed YS, Arif A, Veldhuis SC (2020) Application of the wavelet transform to acoustic emission signals for built-up edge monitoring in stainless steel machining. Measurement 154:107478

    Google Scholar 

  5. Aslan A (2020) Optimization and analysis of process parameters for flank wear, cutting forces and vibration in turning of aisi 5140: A comprehensive study. Measurement 163:107959

    Google Scholar 

  6. Mohanraj T, Shankar S, Rajasekar R, Sakthivel N, Pramanik A (2019) Tool condition monitoring techniques in milling processa review. J Mater Res Technol. https://doi.org/10.1016/j.jmrt.2019.10.031

    Article  Google Scholar 

  7. Zhou Y, Xue W (2018) A multisensor fusion method for tool condition monitoring in milling. Sensors 18(11):3866

    Google Scholar 

  8. Kumar A, Chinnam RB, Tseng F (2019) An hmm and polynomial regression based approach for remaining useful life and health state estimation of cutting tools. Comput Ind Eng 128:1008–1014

    Google Scholar 

  9. Lauro C, Brandão L, Baldo D, Reis R, Davim J (2014) Monitoring and processing signal applied in machining processes–a review. Measurement 58:73–86. https://doi.org/10.1016/j.measurement.2014.08.035

    Article  Google Scholar 

  10. Li Z, Liu R, Wu D (2019) Data-driven smart manufacturing: tool wear monitoring with audio signals and machine learning. J Manuf Process 48:66–76. https://doi.org/10.1016/j.jmapro.2019.10.020

  11. Kannatey-Asibu E, Yum J, Kim T (2017) Monitoring tool wear using classifier fusion. Mech Syst Signal Process 85:651–661

    Google Scholar 

  12. Meng H, Li Y-F (2019) A review on prognostics and health management (phm) methods of lithium-ion batteries. Renew Sust Energ Rev 116:109405

    Google Scholar 

  13. An Q, Tao Z, Xu X, El Mansori M, Chen M (2020) A data-driven model for milling tool remaining useful life prediction with convolutional and stacked lstm network. Measurement 154:107461

    Google Scholar 

  14. Vakharia V, Pandya S, Patel P (2018) Tool wear rate prediction using discrete wavelet transform and k-star algorithm. Life Cycle Reliability and Safety Engineering 7(3):115–125

    Google Scholar 

  15. Zang C, Imregun M (2001) Combined neural network and reduced frf techniques for slight damage detection using measured response data. Arch Appl Mech 71(8):525–536

    MATH  Google Scholar 

  16. Zhou J-T, Zhao X, Gao J (2019) Tool remaining useful life prediction method based on lstm under variable working conditions. Int J Adv Manuf Technol 104(9–12):4715–4726

    Google Scholar 

  17. Sun H, Zhang J, Mo R, Zhang X (2020) In-process tool condition forecasting based on a deep learning method. Robot Comput Integr Manuf 64:101924

    Google Scholar 

  18. Zhou Y, Sun B, Sun W (2020) A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling. Measurement 166:108186

    Google Scholar 

  19. Su W, Lei Z (2019) Mold-level prediction based on long short-term memory model and multi-mode decomposition with mutual information entropy. Adv Mech Eng 11(12):1687814019894433

    Google Scholar 

  20. Cao H, Kang T, Chen X (2019) Noise analysis and sources identification in machine tool spindles. CIRP J Manuf Sci Technol 25:26–35

    Google Scholar 

  21. Xu Z, Li C, Yang Y (2020) Fault diagnosis of rolling bearing of wind turbines based on the variational mode decomposition and deep convolutional neural networks. Appl Soft Comput 95:106515

    Google Scholar 

  22. Sharma V, Parey A (2020) Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed. Eng Fail Anal 107:104204

    Google Scholar 

  23. Lahmiri S (2014) Comparative study of ecg signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthcare Technol Lett 1(3):104–109

    Google Scholar 

  24. Cai W, Zhang W, Hu X, Liu Y (2020) A hybrid information model based on long short-term memory network for tool condition monitoring. J Intell Manuf 1–14

  25. Xia T, Song Y, Zheng Y, Pan E, Xi L (2020) An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation. Comput Ind 115:103182

    Google Scholar 

  26. Zhang J, Wang P, Yan R, Gao RX (2018) Long short-term memory for machine remaining life prediction. J Manuf Syst 48:78–86

    Google Scholar 

  27. Wang Q, Bu S, He Z, Dong ZY (2020) Toward the prediction level of situation awareness for electric power systems using cnn-lstm network. IEEE Trans Ind Inf

  28. Cnc milling tool wear dataset (2010) https://www.kaggle.com/rabahba/phm-data-challenge-2010

  29. Agogino A, Goebel K (2007) Mill data set. best lab, uc berkeley. nasa ames prognostics data repository, nasa ames, moffett field, ca. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/

  30. Zamudio-Ramirez I, Antonino-Daviu JA, Trejo-Hernandez M, Osornio-Rios RAA (2020) Cutting tool wear monitoring in cnc machines based in spindle-motor stray flux signals. IEEE Trans Ind Inf

  31. Hesser DF, Markert B (2019) Tool wear monitoring of a retrofitted cnc milling machine using artificial neural networks. Manuf Lett 19:1–4

    Google Scholar 

  32. Li W, Liang Y, Wang S (2021) Data driven smart manufacturing technologies and applications. Springer

  33. Dibaj A, Ettefagh MM, Hassannejad R, Ehghaghi MB (2020) A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults. Expert Syst Appl p. 114094

  34. Benbouzid M (Ed.) (2020) Signal processing for fault detection and diagnosis in electric machines and systems. Institution of Engineering and Technology

  35. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

    MathSciNet  MATH  Google Scholar 

  36. Chen X, Yang Y, Cui Z, Shen J (2019) Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy. Energy 174:1100–1109

    Google Scholar 

  37. Wang Z, He G, Du W, Zhou J, Han X, Wang J, He H, Guo X, Wang J, Kou Y (2019) Crude oil risk forecasting: New evidence from multiscale analysis approach. IEEE Access 7:44871–44882

    Google Scholar 

  38. Jiang F, Zhu Z, Li W (2018) An improved VMD with empirical mode decomposition and its application in incipient fault detection of rolling bearing. IEEE Access 6:44483–44493

    Google Scholar 

  39. Ali JB, Saidi L, Harrath S, Bechhoefer E, Benbouzid M (2018) Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning. Appl Acoust 132:167–181

    Google Scholar 

  40. Li Y, Cheng G, Liu C, Chen X (2018) Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks. Measurement 130:94–104

    Google Scholar 

  41. Gu R, Chen J, Hong R, Wang H, Wu W (2020) Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and teager energy operator. Measurement 149:106941

    Google Scholar 

  42. Amirat Y, Benbouzid M, Wang T, Bacha K, Feld G (2018) EEMD-based notch filter for induction machine bearing faults detection. Appl Acoust 133:202–209

    Google Scholar 

  43. Chen J, Hu W, Cao D, Zhang M, Huang Q, Chen Z, Blaabjerg F (2020) Novel data-driven approach based on capsule network for intelligent multi-fault detection in electric motors. IEEE Trans Energy Convers p. 1

  44. Wu C, Jiang P, Ding C, Feng F, Chen T (2019) Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Comput Ind 108:53–61

    Google Scholar 

  45. Li X, Zhang W, Ding Q (2019) Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliab Eng Syst Saf 182:208–218

    Google Scholar 

  46. Qiao Y, Wang Y, Ma C, Yang J (2021) Short-term traffic flow prediction based on 1dcnn-lstm neural network structure. Mod Phys Lett B 35(02):2150042

    MathSciNet  Google Scholar 

  47. Drouillet C, Karandikar J, Nath C, Journeaux A-C, El Mansori M, Kurfess T (2016) Tool life predictions in milling using spindle power with the neural network technique. J Manuf Process 22:161–168

    Google Scholar 

  48. Aghazadeh F, Tahan A, Thomas M (2018) Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process. Int J Adv Manuf Technol 98(9–12):3217–3227

    Google Scholar 

  49. Laddada S, Si-Chaib MO, Benkedjouh T, Drai R (2020) Tool wear condition monitoring based on wavelet transform and improved extreme learning machine. Proc IME C J Mech Eng Sci 234(5):1057–1068

    Google Scholar 

  50. Xia P, Huang Y, Xiao D, Liu C, Shi L (2021) Tool wear prediction under varying milling conditions via temporal convolutional network and auxiliary learning, in. IEEE International Conference on Prognostics and Health Management (ICPHM) 2021:1–6

    Google Scholar 

  51. Jain AK, Lad BK (2015) Predicting remaining useful life of high speed milling cutters based on artificial neural network. In: 2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE). IEEE pp 1–5

  52. Xu J, Yamada K, Seikiya K, Tanaka R, Yamane Y (2014) Effect of different features to drill-wear prediction with back propagation neural network. Precis Eng 38(4):791–798

    Google Scholar 

  53. Zhang C, Zhang H (2016) Modelling and prediction of tool wear using ls-svm in milling operation. Int J Comput Integr Manuf 29(1):76–91

    MathSciNet  Google Scholar 

  54. Wang J, Xie J, Zhao R, Zhang L, Duan L (2017) Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robot Comput Integr Manuf 45:47–58

    Google Scholar 

Download references

Acknowledgements

This study was completed in the Mechanical Structures laboratory of the National Polytechnic school of Algeria by the contribution of all authors, and with financial support from the research division of the polytechnic school.

Funding

Declare that all funding institutions are cited.

Author information

Authors and Affiliations

  1. Ecole Nationale Polytechnique, Laboratoire Genie Mécanique & developpement, Elharrach, 16200, Algiers, Algeria

    Rabah Bazi & Said Rechak

  2. Ecole Militaire Polytechnique, LMS, Bordj Elbahri, 16046, Algiers, Algeria

    Tarak Benkedjouh & Houssem Habbouche

  3. UMR CNRS 6174 - UFC / ENSMM / UTBM Automatic Control and Micro- Mechatronic Systems Department 24, FEMTO-ST Institute, rue Alain Savary, 25000, Besançon, France

    Noureddine Zerhouni

Authors
  1. Rabah Bazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tarak Benkedjouh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Houssem Habbouche
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Said Rechak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Noureddine Zerhouni
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors conceived and designed the study. Rabah BAZI conducted the analysis of experimental data, isolation, extraction, selection of signals, statistical analysis and preparation of manuscript. Houssem Habbouche and Tarak benkedjouh start the preprocessing the collected signals used in the study and the preparation of manuscript. said rechak and noureddine zerhouni contributed to signal analysis and manuscript revisions. All authors approved the final version of the manuscript and agree to be held accountable for the content therein.

Corresponding author

Correspondence to Tarak Benkedjouh.

Ethics declarations

Ethical approval

The authors declare that there is no ethical issue applied to this article.

Consent to participate

The authors declare that all authors have read and approved to submit this manuscript to IJAMT.

Consent to publish

The authors declare that all authors agree to sign the Transfer of Copyright for the Publisher to publish this article upon on acceptance.

Competing interests

The authors declare that there is no conflict of interests regarding the publication of this article.

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

Bazi, R., Benkedjouh, T., Habbouche, H. et al. A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring. Int J Adv Manuf Technol 119, 3803–3817 (2022). https://doi.org/10.1007/s00170-021-08448-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-08448-7

Keywords

Search

Navigation