Skip to main content
SpringerLink
Log in

Remaining useful lifetime estimation for metal-bonded grinding tools using hybrid machine learning

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

Abstract

Grinding with metal-bonded cBN grinding tools enables a long lifetime without any need for redressing. However, the lifetime strongly varies and a precise estimation of the remaining number of parts to be machined is an essential contribution to efficient industrial manufacturing. Previous studies focus mainly on the separation of two or three tool wear categories by the application of traditional machine learning methods while achieving a good accuracy of the prediction. In this work, a tool condition monitoring system is proposed to obtain more than 1500 statistical features from several sensors, which represent the characteristics of the performed grinding process. In contrast to most previous studies, a regression task is formulated to estimate the remaining useful tool lifetime, which is determined by the occurrence of strong grinding burn. Two machine learning concepts are compared in this study comprising an ensemble tree-based method and a custom deep state space model based on recurrent neural networks. Using first the signal-based features only, a good estimation quality for both models could be achieved. In addition, the features are extended by physical knowledge coming from a surrogate model of a kinematic geometrical process model and some well-known physical relationships. Using this hybrid machine learning framework, the same models are evaluated again. The prediction quality of the models could be improved significantly resulting in a mean \(R^{2}\) score of more than 0.95.

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

Similar content being viewed by others

References

  1. Denkena B, Krödel A, Wilckens M (2021) High performance peel grinding of steel shafts using coarse electroplated CBN grinding wheels. Prod Eng 15:717–724. https://doi.org/10.1007/s11740-021-01047-1

  2. Wegener K, Hoffmeister HW, Karpuschewski B, Kuster F, Hahmann WC, Rabiey M (2011) Conditioning and monitoring of grinding wheels. CIRP Annals 60(2):757–777. https://doi.org/10.1016/j.cirp.2011.05.003

    Article  Google Scholar 

  3. Malkin S, Guo C (2008) Grinding technology: theory and application of machining with abrasives, 2nd edn. Industrial Press. ISBN 978-0-83-1192204

  4. Yoshida T, Karasawa H, Fukui R, Fujii K, Warisawa S (2021) Analysis of chip size distribution using image processing technology to estimate wear state of cylindrical grinding wheel. Tribology International 153:106600. https://doi.org/10.1016/j.triboint.2020.106600

    Article  Google Scholar 

  5. Sauter E, Winter M, Sarikaya E, Wegener K (2021) In-process detection of grinding burn using machine learning. Int J Adv Manuf Technol 115:2281–2297. https://doi.org/10.1007/s00170-021-06896-9

    Article  Google Scholar 

  6. Sauter E, Winter M, Wegener K (2022) Analysis of robustness and transferability in feature-based grinding burn detection. Int J Adv Manuf Technol 120:2587–2602. https://doi.org/10.1007/s00170-022-08834-9

    Article  Google Scholar 

  7. Serin G, Sener B, Ozbayoglu AM, Unver HO (2020) Review of tool condition monitoring in machining and opportunities for deep learning. Int J Adv Manuf Technol 109(3–4):953–974. https://doi.org/10.1007/s00170-020-05449-w

    Article  Google Scholar 

  8. Nathan RD, Vijayaraghavan L, Krishnamurthy R (1999) In-process monitoring of grinding burn in the cylindrical grinding of steel. J Mater Process Technol 91(1–3):37–42. https://doi.org/10.1016/S0924-0136(98)00408-7

    Article  Google Scholar 

  9. Lezanski P (2001) An intelligent system for grinding wheel condition monitoring. J Mater Process Technol 109(3):258–263. https://doi.org/10.1016/S0924-0136(00)00808-6

  10. Liao TW, Hua G, Qu J, Blau PJ (2007) Grinding wheel condition monitoring with hidden Markov model-based clustering methods. Mach Sci Technol 10(4):511–538. https://doi.org/10.1080/10910340600996175

  11. Yang Z, Yu Z (2012) Grinding wheel wear monitoring based on wavelet analysis and support vector machine. Int J Adv Manuf Technol 62(1):107–121. https://doi.org/10.1007/s00170-011-3797-1

    Article  Google Scholar 

  12. Arriandiaga A, Portillo E, Sánchez J, Cabanes I, Pombo I (2014) Virtual sensors for on-line wheel wear and part roughness measurement in the grinding process. Sensors 14(5):8756–8778

    Article  Google Scholar 

  13. Arun A, Rameshkumar K, Unnikrishnan D, Sumesh A (2018) Tool condition monitoring of cylindrical grinding process using acoustic emission sensor. Materials Today: Proceedings 5(5, Part 2):11888–11899, 10.1016/j.matpr.2018.02.162, International Conference on Materials Manufacturing and Modelling, ICMMM - 2017, 9–11, March 2017. https://doi.org/10.1016/j.matpr.2018.02.162

  14. Guo W, Li B, Zhou Q (2019) An intelligent monitoring system of grinding wheel wear based on two-stage feature selection and long short-term memory network. Proc Inst Mech Eng 233(13):2436–2446. https://doi.org/10.1177/0954405419840556

    Article  Google Scholar 

  15. Lee CH, Jwo JS, Hsieh HY, Lin CS (2020) An intelligent system for grinding wheel condition monitoring based on machining sound and deep learning. IEEE Access 8:58279–58289. https://doi.org/10.1109/ACCESS.2020.2982800

    Article  Google Scholar 

  16. Zhang B, Katinas C, Shin YC (2020) Robust wheel wear monitoring system for cylindrical traverse grinding. IEEE/ASME Transactions on Mechatronics 25(5):2220–2229. https://doi.org/10.1109/TMECH.2020.3007047

    Article  Google Scholar 

  17. Rameshkumar K, Mouli D, Shivith K (2021) Machine learning models for predicting grinding wheel conditions using acoustic emission features. SAE Int J Manuf Mater Mech 14(4):387–406. https://doi.org/10.4271/05-14-04-0026

    Article  Google Scholar 

  18. Mahata S, Shakya P, Babu NR (2021) A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform. Prec Eng 70:77–91. https://doi.org/10.1016/j.precisioneng.2021.01.009

    Article  Google Scholar 

  19. Sachin Krishnan P, Rameshkumar K (2021) Grinding wheel condition prediction with discrete hidden Markov model using acoustic emission signature. Materials Today: Proceedings 46:9168–9175. Int Mech Eng Congress 2019. https://doi.org/10.1016/j.matpr.2019.12.428

  20. Yin C, Wang Y, Lee HP, He J, He Y, Sun Y (2021) Robust wheel wear detection for solid carbide grinding under strong noise interference: a new approach based on improved ensemble noise-reconstructed empirical mode decomposition. Wear 486–487. https://doi.org/10.1016/j.wear.2021.204112

  21. Brinksmeier E, Aurich J, Govekar E, Heinzel C, Hoffmeister HW, Klocke F, Peters J, Rentsch R, Stephenson D, Uhlmann E, Weinert K, Wittmann M (2006) Adv Model Simul Grind Process CIRP Annals 55(2):667–696. https://doi.org/10.1016/j.cirp.2006.10.003

  22. Winter M (2016) Eco-efficiency of grinding processes and systems. Springer, Cham. https://doi.org/10.1007/978-3-319-25205-6

  23. Darafon A (2013) Measuring and modeling of grinding wheel topography. PhD thesis, Dalhousie University. http://hdl.handle.net/10222/21678. Accessed 19 Oct 2021

  24. Rabiey M (2011) Dry grinding with CBN wheels, the effect of structuring. PhD thesis, University of Stuttgart. https://doi.org/10.18419/opus-4429

  25. Wang D, Ge P, Bi W, Jiang J (2014) Grain trajectory and grain workpiece contact analyses for modeling of grinding force and energy partition. Int J Adv Manuf Technol 70(9):2111–2123. https://doi.org/10.1007/s00170-013-5428-5

    Article  Google Scholar 

  26. Rowe WB (2009) Principles of modern grinding technology, 1st edn. William Andrew. https://books.google.de/books?id=x1wdlQEACAAJ

  27. Jermolajev S, Epp J, Heinzel C, Brinksmeier E (2016) Material modifications caused by thermal and mechanical load during grinding. In: 3rd CIRP Conference on Surface Integrity 45:43–46. https://doi.org/10.1016/j.procir.2016.02.159

  28. Spriggs GE (2002) Properties of diamond and cubic boron nitride: datasheet from Landolt-Börnstein, vol 2A2. Springer-Verlag, Berlin Heidelberg, 13:102–107. https://doi.org/10.1007/108586417

  29. Deutsche Edelstahlwerke GmbH (2011) Datenblatt. Online. https://www.dew-stahl.com/fileadmin/files/dew-stahl.com/documents/Publikationen/Werkstoffdatenblaetter/Baustahl/1.1191_1.1201_de.pdf. Accessed 9 Nov 2022

  30. Usui E, Shirakashi T, Kitagawa T (1984) Analytical prediction of cutting tool wear. Wear 100(1):129–151. https://doi.org/10.1016/0043-1648(84)90010-3

  31. Kuffa M, Kuster F, Wegener K (2017) Stochastic kinematic process model with an implemented wear model for high feed dry grinding. Inventions 2(4):31. https://doi.org/10.3390/inventions2040031

    Article  Google Scholar 

  32. Cavazzuti M (2013) Optimization methods. Springer, Berlin Heidelberg. https://doi.org/10.1007/978-3-642-31187-1

  33. Bouhlel MA, Hwang JT, Bartoli N, Lafage R, Morlier J, Martins JRRA (2019) A Python surrogate modeling framework with derivatives. Adv Eng Softw 135. https://doi.org/10.1016/j.advengsoft.2019.03.005

  34. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. CoRR. https://doi.org/10.1145/2939672.2939785

  35. Krishnan RG, Shalit U, Sontag D (2016) Structured inference networks for nonlinear state space models. In: Thirty-First AAAI Conference on Artificial Intelligence. https://doi.org/10.48550/ARXIV.1609.09869

  36. ISO14104:2017(E), (2017) Gears surface temper etch inspection after grinding, chemical method. Standard, International Organization for Standardization

  37. Naik DN, Mathew NT, Vijayaraghavan L (2019) Wear of electroplated super abrasive CBN wheel during grinding of Inconel 718 super alloy. J Manuf Process 43:1–8. https://doi.org/10.1016/j.jmapro.2019.04.033

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Machine Tools and Manufacturing (IWF), Department of Mechanical Engineering, ETH Zurich, Leonhardstrasse 21, Zurich, 8092, Switzerland

    Emil Sauter & Konrad Wegener

  2. Corporate Research, Robert Bosch GmbH, Robert-Bosch-Campus 1, Renningen, 71272, Germany

    Emil Sauter, Hanyu Sun & Marius Winter

Authors
  1. Emil Sauter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanyu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marius Winter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Konrad Wegener
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emil Sauter.

Ethics declarations

Conflict of interest

On parts of this work, a patent was filed.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Sauter, E., Sun, H., Winter, M. et al. Remaining useful lifetime estimation for metal-bonded grinding tools using hybrid machine learning. Int J Adv Manuf Technol 123, 3243–3260 (2022). https://doi.org/10.1007/s00170-022-10260-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10260-w

Keywords

Search

Navigation