SpringerLink
Log in

Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning

  • Special Issue on Multi-modal Information Learning and Analytics on Big Data
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

When the turning tool has worn and failed but the failure is not found, if it continues to be used for processing, it will break, and cause the workpiece to be scrapped, and even damage the machine tool. In order to avoid the loss caused by turning tool wear, the remaining useful life (RUL) prediction of turning tool wear has become a hot research topic in recent years. For RUL prediction in turning tools, the traditional machine is difficult to acquire sufficient degradation data and inconsistent data distribution among different turning tools in engineering, and they cannot provide better prediction accuracy to some extent. To solve the above problems, this paper proposes a multi-granularity feature extraction (MGFE) method based on the gray-level co-occurrence matrix (GLCM) and random forest (RF). Moreover, a health indicator (HI) of turning tools in the source domain was obtained. The common representative features in HI sequence of target domain was transferred to source domain and builds the condition monitoring and life prediction system of turning tools based on extreme learning machine and transfer learning. Finally, extreme vector machine (ELM) is used to construct the RUL prediction model. The research results show that the model constructed in this paper is effective in RUL prediction and can significantly improve the prediction accuracy of remaining useful life.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

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

Similar content being viewed by others

References

  1. Karandikar JM, Abbas AE, Schmitz TL (2014) Tool life prediction using Bayesian updating. Part 2: turning tool life using a Markov Chain Monte Carlo approach. Precis Eng 38(1):18–27

    Article  Google Scholar 

  2. Yin Z, Huang C, Yuan J et al (2015) Cutting performance and life prediction of an Al2O3/TiC micro–nano-composite ceramic tool when machining austenitic stainless steel. Ceram Int 41(5):7059–7065

    Article  Google Scholar 

  3. Karandikar JM, Abbas AE, Schmitz TL (2014) Tool life prediction using Bayesian updating. Part 1: milling tool life model using a discrete grid method. Precis Eng 38(1):9–17

    Article  Google Scholar 

  4. Benkedjouh T, Medjaher K, Zerhouni N et al (2015) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 26(2):213–223

    Article  Google Scholar 

  5. Krolczyk GM, Nieslony P, Legutko S (2015) Determination of tool life and research wear during duplex stainless steel turning. Arch Civ Mech Eng 15(2):347–354

    Article  Google Scholar 

  6. Qin Yi, **ang S, Chai Yi, Chen H (2020) Macroscopic-microscopic attention in LSTM networks based on fusion features for gear remaining life prediction. IEEE Trans Ind Electron 67(12):10865–10875

    Article  Google Scholar 

  7. Dong M, He D (2007) A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mech Syst Signal Process 21(5):2248–2266

    Article  MathSciNet  Google Scholar 

  8. Shen ZJ, Chen XF, He ZJ et al (2013) Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine. J Mech Eng (in Chinese) 49(2):183–189

    Article  Google Scholar 

  9. Mao W, He J, Zuo MJ (2019) Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Trans Instrum Meas PP(99):1–1

    Google Scholar 

  10. Mosallam A, Medjaher K, Zerhouni N (2016) Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. J Intell Manuf 27(5):1037–1048

    Article  Google Scholar 

  11. Shihab SK, Khan ZA, Mohammad A et al (2014) A review of turning of hard steels used in bearing and automotive applications. Prod Manuf Res 2(1):24–49

    Google Scholar 

  12. Kim DM, Bajpai V, Kim BH et al (2015) Finite element modeling of hard turning process via a micro-textured tool. Int J Adv Manuf Technol 78(9–12):1393–1405

    Article  Google Scholar 

  13. Ahmadzadeh F, Lundberg J (2014) Remaining useful life estimation. Int J Syst Assur Eng Manag 5(4):461–474

    Article  Google Scholar 

  14. Gupta MK, Sood PK (2017) Surface roughness measurements in NFMQL assisted turning of titanium alloys: an optimization approach. Friction 5(2):155–170

    Article  Google Scholar 

  15. Das SR, Dhupal D, Kumar A (2015) Study of surface roughness and flank wear in hard turning of AISI 4140 steel with coated ceramic inserts. J Mech Sci Technol 29(10):4329–4340

    Article  Google Scholar 

  16. Kumar R, Sahoo AK, Mishra PC et al (2018) Comparative investigation towards machinability improvement in hard turning using coated and uncoated carbide inserts: part I experimental investigation. Adv Manuf 6(1):52–70

    Article  Google Scholar 

  17. Mia M, Dhar NR (2017) Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method. Int J Adv Manuf Technol 88(1–4):739–753

    Article  Google Scholar 

  18. Leo Kumar SP, Jerald J, Kumanan S et al (2014) A review on current research aspects in tool-based micromachining processes. Mater Manuf Process 29(11–12):1291–1337

    Article  Google Scholar 

  19. Fernández-Valdivielso A, López de Lacalle LN, Urbikain G et al (2016) Detecting the key geometrical features and grades of carbide inserts for the turning of nickel-based alloys concerning surface integrity. Proc Inst Mech Eng Part C J Mech Eng Sci 230(20):3725–3742

    Article  Google Scholar 

  20. Bensouilah H, Aouici H, Meddour I et al (2016) Performance of coated and uncoated mixed ceramic tools in hard turning process. Measurement 82:1–18

    Article  Google Scholar 

  21. Gupta M, Kumar S (2015) Investigation of surface roughness and MRR for turning of UD-GFRP using PCA and Taguchi method. Eng Sci Technol Int J 18(1):70–81

    MathSciNet  Google Scholar 

  22. Madariaga A, Esnaola JA, Fernandez E et al (2014) Analysis of residual stress and work-hardened profiles on Inconel 718 when face turning with large-nose radius tools. Int J Adv Manuf Technol 71(9–12):1587–1598

    Article  Google Scholar 

  23. Frangopol DM, Soliman M (2016) Life-cycle of structural systems: recent achievements and future directions. Struct Infrastruct Eng 12(1):1–20

    Article  Google Scholar 

  24. Javed K, Gouriveau R, Zerhouni N et al (2014) Enabling health monitoring approach based on vibration data for accurate prognostics. IEEE Trans Ind Electron 62(1):647–656

    Article  Google Scholar 

  25. Sun S, Brandt M, Mo JPT (2014) Evolution of tool wear and its effect on cutting forces during dry machining of Ti–6Al–4V alloy. Proc Inst Mech Eng Part B J Eng Manuf 228(2):191–202

    Article  Google Scholar 

  26. Khorasani AM, Yazdi MRS (2017) Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. Int J Adv Manuf Technol 93(1–4):141–151

    Article  Google Scholar 

  27. Pervaiz S, Rashid A, Deiab I et al (2014) Influence of tool materials on machinability of titanium-and nickel-based alloys: a review. Mater Manuf Process 29(3):219–252

    Article  Google Scholar 

  28. Suresh P, Marimuthu K, Ranganathan S et al (2014) Optimization of machining parameters in turning of Al–SiC–Gr hybrid metal matrix composites using grey-fuzzy algorithm. Trans Nonferrous Met Soc China 24(9):2805–2814

    Article  Google Scholar 

  29. Guo L, Lei YG, Li NP et al (2018) Machinery health indicator construction based on convolutional neural networks considering trend burr. Neurocomputing 292(31):142–150

    Article  Google Scholar 

  30. Liu Z, Zuo MJ, Qin Y (2016) Remaining useful life prediction of rolling element bearings based on health state assessment. Proc Inst Mech Eng C J Mech Eng Sci 230(2):314–330

    Article  Google Scholar 

  31. Wu Y, Yuan M, Dong S, Lin L et al (2018) Remaining useful life estimation of engineered systems using vanilla lstm neural networks. Neurocomputing 275:167–179

    Article  Google Scholar 

  32. Deutsch J, He D (2017) Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Trans Syst Man Cybern Syst 48(1):11–20

    Article  Google Scholar 

  33. Reshef DN, Reshef YA, Finucane HK et al (2011) Detectingnovel associations in large data sets. Science (New York, NY) 334(6062):1518–1524

    Article  Google Scholar 

Download references

Acknowledgements

Information of funding: Subsidized by Chongqing Basic Science and Research Project (cstc2015jcyjbx0133), National Natural Science Foundation of China (NSFC 51375519), National Natural Science Foundation of China (NSFC 51975078).

Author information

Authors and Affiliations

  1. Department of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, 400074, China

    Zhan Gao, Qiguo Hu & **angyang Xu

Authors
  1. Zhan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiguo Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. **angyang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhan Gao or Qiguo Hu.

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

Gao, Z., Hu, Q. & Xu, X. Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning. Neural Comput & Applic 34, 3399–3410 (2022). https://doi.org/10.1007/s00521-021-05716-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05716-1

Keywords

Search

Navigation