Skip to main content

Advertisement

SpringerLink
Log in

A new method based on a WOA-optimized support vector machine to predict the tool wear

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

Abstract

Tool wear has been a great impact on machining quality and machining efficiency during cutting. The serious tool wear will even lead to workpiece failure and catastrophic equipment failure. Accurate and effective tool wear monitoring is important to evaluate the degree of tool wear, replace tools in time, and promote the intelligent development of the manufacturing industry. To improve the accuracy of online prediction of tool wear, a new method based on whale optimization algorithm (WOA) optimized support vector machine (SVM) is proposed to predict the tool wear. Specifically, the multi-domain features of cutting force and vibration signals are extracted based on the time domain, frequency domain, and time–frequency domain, and the signal sensitive features closely related to tool wear are selected by the Pearson correlation coefficient method. SVM is applied to predict the evolution of tool wear. WOA is used to improve prediction accuracy by optimizing the internal parameters of SVM. By learning the nonlinear correlation between sensitive features and tool wear, a model for predicting tool wear based on WOA-SVM is constructed to predict the change of tool wear value. The effectiveness and prediction performance of the proposed method are verified by milling experiments. Results show that this method can predict tool wear value based on limited historical data information accurately and effectively. Compared with SVM prediction methods optimized by some common optimization algorithms (particle swarm optimization (PSO) and genetic algorithm (GA)), the prediction accuracy and stability are higher and the generalization is stronger. These findings may be of great significance for the improvement of machining quality and efficiency of parts, the stable operation of manufacturing system, and the intelligent development of manufacturing industry.

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

Similar content being viewed by others

Availability of data and materials

The data that support the results of this article are included within the article.

References

  1. Cao XL, Shen B, Guo CY, Wang L (2018) A new era for the development of advanced manufacturing industry-intelligent manufacturing. Northern economy 369(8):58–60

    Google Scholar 

  2. Yousefi S, Zohoor M (2019) Effect of cutting parameters on thedimensional accuracy and surface finish in the hard turning of MDN250 steel with cubic boron nitride tool for developing aknowledge based expert system. Int J Mech Mater Eng 14(1):40712. https://doi.org/10.1186/s40712-018-0097-7

    Article  Google Scholar 

  3. Liu XL, Liu SY, Li XB, Zhang BW, Yue CX, Liang SY (2021) Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network. J Manuf Syst 60:608–619. https://doi.org/10.1016/j.jmsy.2021.06.006

    Article  Google Scholar 

  4. Baig RU, Javed S, Khaisar M, Shakoor M (2021) Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy. Adv Mech Eng 13(6):1–14. https://doi.org/10.1177/16878140211026720

    Article  Google Scholar 

  5. Caggiano A, Nele L (2018) Artificial neural networks for tool wear prediction based on sensor fusion monitoring of CFRP/CFRP stack drilling. Int J Autom Tech 12(3):275–281

    Article  Google Scholar 

  6. Wiciak-Pikua M, Felusiak-Czyryca A, Twardowski P (2020) Tool wear prediction based on artificial neural network during aluminum matrix composite milling. Sensors 20(20):5798. https://doi.org/10.3390/s20205798

    Article  Google Scholar 

  7. Twardowski P, Wiciak-Pikuła M (2019) Prediction of tool wear using artificial neural networks during turning of hardened steel. Materials 12(19):3091–3106. https://doi.org/10.3390/ma12193091

    Article  Google Scholar 

  8. Bagga PJ, Makhesana MA, Patel HD, Patel KM (2021) Indirect method of tool wear measurement and prediction using ANN network in machining process. Materials Today Proceedings 44(6):1549–1554. https://doi.org/10.1016/j.matpr.2020.11.770

    Article  Google Scholar 

  9. Wang WG (2017) Cutting tool wear condition monitoring based on artificial bee colony-BP neutral network algorithm. Journal of Mechanical Strength 39(6):1282–1287

    Google Scholar 

  10. Xu LH, Huang CZ, Li CW, Wang J, Liu HL, Wang XD (2020) Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining. J Intell Manuf 32(1–4):77–90. https://doi.org/10.1007/s10845-020-01559-0

    Article  Google Scholar 

  11. Zhang HY, Zhang C, Zhang JL, Zhou LS (2014) Tool wear model based on least squares support vector machines and Kalman filter. Prod Eng Res Devel 8(1–2):101–109. https://doi.org/10.1007/s11740-014-0527-1

    Article  Google Scholar 

  12. Nie P, He C, Xu L, Cui KQ (2015) The prediction research of tool VB value based on principal component analysis and SVR. 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering

  13. Xiao PF, Zhang CY, Luo M, Liu WW (2018) Modeling method for tool wear prediction based on ADNLSSVM. China Mechanical Engineering 29(7):842–849

    Google Scholar 

  14. Gomes MC, Brito LC, Silva MBD, Duarte M (2021) Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors. Precis Eng 67:137–151. https://doi.org/10.1016/j.precisioneng.2020.09.025

    Article  Google Scholar 

  15. Wang TY, He HL, Wang GF, Leng YG, Xu YG, Li Q (2007) Fault diagnosis of rolling bearing based on empirical mode decomposition and least squares support vector machine. Chin J Mech Eng 43(4):88–92

    Article  Google Scholar 

  16. Nie P, He C, Xu L, Li ZQ, Cui KQ (2015) Research on tool VB value prediction of optimized SVM based on genetic algorithm. Machine Tool & Hydraulics 43(11):43–45

    Google Scholar 

  17. Zhang KF, Yuan HQ, Nie P (2015) A method for tool condition monitoring based on sensor fusion. J Intell Manuf 26(5):1–16. https://doi.org/10.1007/s10845-015-1112-y

    Article  Google Scholar 

  18. García-Nieto PJ, García-Gonzalo E, Vilán-Vilán JA, Segade A (2016) A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data. Int J Adv Manuf Technol 86(1–4):769–780. https://doi.org/10.1007/s00170-015-8148-1

    Article  Google Scholar 

  19. Pandiyan V, Caesarendra W, Tjahjowidodo T, Tan HH (2018) In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. J Manuf Process 31:199–213. https://doi.org/10.1016/j.jmapro.2017.11.014

    Article  Google Scholar 

  20. Kong DD, Chen YJ, Li N, Chen DX (2019) Tool wear estimation in end-milling of titanium alloy using NPE and a novel WOA-SVM model. IEEE Trans Instrum Meas 69(7):5219–5232. https://doi.org/10.1109/TIM.2019.2952476

    Article  Google Scholar 

  21. Safavi HR, Esmikhani M (2013) Conjunctive use of surface waterand groundwater: application of support vector machines (SVMs) and genetic algorithms. Water Resour Manag 27(7):2623–2644. https://doi.org/10.1007/s11269-013-0307-2

    Article  Google Scholar 

  22. Shan ZD, Zhu FX (2018) Wear mechanism and prediction model of polycrystalline diamond tool in milling sand mould. Chin J Mech Eng 54(17):124–132

    Article  Google Scholar 

  23. Shi DF, Gindy NN (2007) Tool wear predictive model based on least squares support vector machines. Mech Syst Signal Proc 21(4):1799–1814. https://doi.org/10.1016/j.ymssp.2006.07.016

    Article  Google Scholar 

  24. Seyedali M, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95(5):51–67

    Google Scholar 

  25. PHM Society (2010) 2010 PHM society conference data challenge. https://www.phmsociety.org/competition/phm/10

  26. Chen N, Hao BJ, Guo YL, Li L, Khan AM, He N (2020) Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach. Int J Adv Manuf Technol 108(1–4):1–11. https://doi.org/10.1007/s00170-020-05549-7

    Article  Google Scholar 

  27. Huang ZW, Zhu JG, Lei JT, Li XR, Tian FQ (2019) Tool wear predicting based on multisensory raw signals fusion by reshaped time series convolutional neural network in manufacturing. IEEE Access 7:178640–178651. https://doi.org/10.1109/ACCESS.2019.2958330

    Article  Google Scholar 

  28. Huang ZW, Zhu JG, Lei JT, Li XR, Tian FQ (2020) Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. J Intell Manuf 31(9–12):1–14. https://doi.org/10.1007/s10845-019-01488-7

    Article  Google Scholar 

  29. Razak NH, Chen ZW, Pasang T (2016) Progression of tool deterioration and related cutting force during milling of 718Plus superalloy using cemented tungsten carbide tools. Int J Adv Manuf Technol 86(9–12):3203–3216. https://doi.org/10.1007/s00170-016-8438-2

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the anonymous reviewers for valuable comments and suggestions, which helped to improve this study.

Funding

This work was financially supported by National Natural Science Foundation of China (No. 52175394) and Joint guidance project of Heilongjiang Natural Science Foundation (No. LH2021E083).

Author information

Authors and Affiliations

  1. The Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, People’s Republic of China

    Yaonan Cheng, Xiaoyu Gai, Yingbo Jin, Rui Guan, Mengda Lu & Ya Ding

Authors
  1. Yaonan Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyu Gai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingbo Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mengda Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ya Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study conception and design: Yaonan Cheng. Drafting of manuscript: Xiaoyu Gai. Method proposed and interpretation of data: Xiaoyu Gai and Yingbo Jin. Acquisition and analysis of data: Rui Guan, Mengda Lu, and Ya Ding.

Corresponding author

Correspondence to Yaonan Cheng.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

The authors have consent to participate.

Consent for publication

The authors have consent to publish.

Competing interests

The authors declare no competing interests.

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

Cheng, Y., Gai, X., Jin, Y. et al. A new method based on a WOA-optimized support vector machine to predict the tool wear. Int J Adv Manuf Technol 121, 6439–6452 (2022). https://doi.org/10.1007/s00170-022-09746-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-09746-4

Keywords

Search

Navigation