Skip to main content
SpringerLink
Log in

Tool wear predicting based on weighted multi-kernel relevance vector machine and probabilistic kernel principal component analysis

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

Abstract

This paper proposes a novel tool wear predicting method based on a weighted multi-kernel relevance vector machine (WMKRVM) and the integrated radial basis function–based probabilistic kernel principal component analysis (PKPCA_IRBF). The proposed WMKRVM model is constructed using the optimized standard single kernel RVM and its weight parameters. As a new dimension increment technique, PKPCA_IRBF can extract the noise information of the cutting force signal feature and incorporate the noise information into the model. Moreover, PKPCA_IRBF is first proposed to fuse the cutting force signal feature to improve the confidence interval provided by the WMKRVM model. Compared to the traditional PKPCA_RBF method, PKPCA_IRBF has a broader range of kernel parameter selection intervals and higher model accuracy. The cutting experiment is carried out to validate the effectiveness of the proposed tool wear predicting technique. Experimental results show that the proposed tool wear predicting technique can accurately monitor the tool wear width with strong robustness under various cutting conditions, laying the foundation for application in the industrial field.

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

References

  1. Dimla DE (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40:1073–1098. https://doi.org/10.1016/S0890-6955(99)00122-4

    Article  Google Scholar 

  2. Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. The International Journal of Advanced Manufacturing Technology 26:693–710. https://doi.org/10.1007/s00170-004-2038-2

    Article  Google Scholar 

  3. Teti R, Jemielniak K, Donnell OG, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann 59:717–739. https://doi.org/10.1016/j.cirp.2010.05.010

    Article  Google Scholar 

  4. Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65:371–393

  5. Liu T, Zhu K (2021) A switching hidden semi-Markov model for degradation process and its application to time-varying tool wear monitoring. IEEE Trans Industr Inf 17:2621–2631. https://doi.org/10.1109/TII.2020.3004445

    Article  Google Scholar 

  6. Han D, Yu J, Tang D (2021) An HDP-HMM based approach for tool wear estimation and tool life prediction. Qual Eng 33:208–220. https://doi.org/10.1080/08982112.2020.1813760

    Article  Google Scholar 

  7. Huang C, Yin X, Huang H, Li Y (2020) An enhanced deep learning-based fusion prognostic method for RUL prediction. IEEE Trans Reliab 69:1097–1109. https://doi.org/10.1109/TR.2019.2948705

    Article  Google Scholar 

  8. Sun H, Zhang J, Mo R, Zhang X (2020) In-process tool condition forecasting based on a deep learning method. Robotics and Computer-Integrated Manufacturing 64:101924. https://doi.org/10.1016/j.rcim.2019.101924

    Article  Google Scholar 

  9. Xu L, Huang C, Li C, Wang J, Liu H, Wang X (2021) Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining. J Intell Manuf 32:77–90. https://doi.org/10.1007/s10845-020-01559-0

    Article  Google Scholar 

  10. 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 Industr Electron 66:3794–3803. https://doi.org/10.1109/TIE.2018.2856193

    Article  Google Scholar 

  11. Wang G, Zhang Y, Liu C, Xie Q, Xu Y (2019) A new tool wear monitoring method based on multi-scale PCA. J Intell Manuf 30:113–122. https://doi.org/10.1007/s10845-016-1235-9

    Article  Google Scholar 

  12. Huang Z, Zhu J, Lei J, Li X, Tian F (2020) Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. J Intell Manuf 31:953–966. https://doi.org/10.1007/s10845-019-01488-7

    Article  Google Scholar 

  13. Shen Y, Yang F, Habibullah MS, Ahmed J, Das AK, Zhou Y, Ho CL (2021) Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques. J Intell Manuf 32:1753–1766. https://doi.org/10.1007/s10845-020-01625-7

    Article  Google Scholar 

  14. Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244. https://doi.org/10.1162/15324430152748236

    Article  MathSciNet  MATH  Google Scholar 

  15. Wang G, Yang Y, Xie Q, Zhang Y (2014) Force based tool wear monitoring system for milling process based on relevance vector machine. Adv Eng Softw 71:46–51. https://doi.org/10.1016/j.advengsoft.2014.02.002

    Article  Google Scholar 

  16. Kong D, Chen Y, Li N, Duan C, Lu L, Chen D (2019) Relevance vector machine for tool wear prediction. Mech Syst Signal Process 127:573–594. https://doi.org/10.1016/j.ymssp.2019.03.023

    Article  Google Scholar 

  17. Chen F, Cheng M, Tang B, Xiao W, Chen B, Shi X (2020) A novel optimized multi-kernel relevance vector machine with selected sensitive features and its application in early fault diagnosis for rolling bearings. Measurement 156:107583. https://doi.org/10.1016/j.measurement.2020.107583

    Article  Google Scholar 

  18. Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering 8:22–34. https://doi.org/10.1080/21642583.2019.1708830

    Article  Google Scholar 

  19. Wang J, Xie J, Zhao R, Zhang L, Duan L (2017) Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics and Computer-Integrated Manufacturing 45:47–58. https://doi.org/10.1016/j.rcim.2016.05.010

    Article  Google Scholar 

  20. Ge Z, Song Z (2010) Kernel generalization of PPCA for nonlinear probabilistic monitoring. Ind Eng Chem Res 49:11832–11836. https://doi.org/10.1021/ie100852s

    Article  Google Scholar 

  21. Kim D, Lee I (2003) Process monitoring based on probabilistic PCA. Chemom Intell Lab Syst 67:109–123. https://doi.org/10.1016/S0169-7439(03)00063-7

    Article  Google Scholar 

  22. Neil DL (2005) Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J Mach Learn Res 6

  23. Tanaka A, Imai H, Kudo M, Miyakoshi M (2007) Integrated kernels and their properties. Pattern Recogn 40:2930–2938. https://doi.org/10.1016/j.patcog.2007.02.014

    Article  MATH  Google Scholar 

  24. Zhang Z, Wang G, Yeung DY, Kwok JT (2004) Probabilistic kernel principal component analysis

  25. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  26. Yang XS (2010) Engineering optimization (an introduction with metaheuristic applications) || Engineering Optimization

  27. Jamil M, Yang X (2013) A literature survey of benchmark functions for global optimization problems[Z]. Cornell University Library, arXiv.org, Ithaca

    MATH  Google Scholar 

  28. Yang XS (2014) [Studies in Computational Intelligence] Cuckoo Search and Firefly Algorithm Volume 516 || Intelligent Firefly Algorithm for Global Optimization. In:315–330

  29. Sm A, Smm B, Al A (2014) Grey wolf optimizer. Advances in Engineering Software:46–61

  30. Eberhart, SY (2002) Particle swarm optimization: developments, applications and resources: Congress on Evolutionary Computation

  31. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

Download references

Funding

This research was supported by the Key R & D project of Shandong Province (No. 2019JZZY010445).

Author information

Authors and Affiliations

  1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education of China, School of Mechanical Engineering, Shandong University, Jinan, China

    Guohao Song, Jianhua Zhang, Yingshang Ge, Kangyi Zhu & Zhensheng Fu

  2. School of Mechanical Engineering, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, China

    Guohao Song, Jianhua Zhang, Yingshang Ge, Kangyi Zhu & Zhensheng Fu

  3. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, People’s Republic of China

    Luchuan Yu

Authors
  1. Guohao Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingshang Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kangyi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhensheng Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luchuan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhua Zhang.

Ethics declarations

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

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, G., Zhang, J., Ge, Y. et al. Tool wear predicting based on weighted multi-kernel relevance vector machine and probabilistic kernel principal component analysis. Int J Adv Manuf Technol 122, 2625–2643 (2022). https://doi.org/10.1007/s00170-022-09762-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-09762-4

Keywords

Search

Navigation