Skip to main content
SpringerLink
Log in

Prediction of Tool Tip Dynamics Through Machine Learning and Inverse Receptance Coupling

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

The monitoring of cutting forces during machining operations can provide information pertaining to tool wear and deflection. The cutting forces are influenced by the cutting parameters and system dynamics. To increase the efficiency of the cutting operations and to improve the quality of the finished products, the cutting parameters must be established within the stable limits of the machine tool. The identification of the dynamics of machine tools is therefore critical for ensuring a fast, efficient, and safe cutting operation. While the receptance coupling (RC) method is commonly utilized to determine the dynamics of machine tools and the chatter stability limits, its application necessitates multiple experiments in conjunction with finite element (FE) simulations, which can be complex and time-consuming to perform on a factory floor. To address these challenges, this study investigates the application of the artificial neural networks (ANNs) for predicting the frequency response functions (FRFs) of the tooling assembly. Multiple ANNs are trained to predict the FRFs of the tool-tool holder assembly, resulting in a significant reduction in the simulation time. The input and output training sets are generated using a validated FE software. Once the ANNs are trained, the predicted FRFs of the tooling assembly are combined with the FRFs of the machine through the RC method to obtain the tool tip dynamics.

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

References

  1. Quintana, G., & Ciurana, J. (2011). Chatter in machining processes: A review. International Journal of Machine Tools and Manufacture, 51(5), 363–376.

    Article  Google Scholar 

  2. Mostaghimi, H., Park, C. I., Kang, G., Park, S. S., & Lee, D. Y. (2021). Reconstruction of cutting forces through fusion of accelerometer and spindle current signals. Journal of manufacturing processes, 68, 990–1003.

    Article  Google Scholar 

  3. Schmitz, T., Betters, E., Budak, E., Yüksel, E., Park, S., & Altintas, Y. (2022). Review and status of tool tip frequency response function prediction using receptance coupling. Precision Engineering, 79, 60–77.

    Article  Google Scholar 

  4. Nam, S., Hayasaka, T., Jung, H., & Shamoto, E. (2021). Proposal of novel spindle speed variation profile with constant acceleration rate for improvement of chatter stability. Precision Engineering, 68, 218–234.

    Article  Google Scholar 

  5. Zhu, L., & Liu, C. (2020). Recent progress of chatter prediction, detection and suppression in milling. Mechanical Systems and Signal Processing, 143, 106840.

    Article  Google Scholar 

  6. Hayasaka, T., Nam, S., Jung, H., Shamoto, E., & Saito, K. (2018). Proposal of accelerative cutting for suppression of regenerative chatter. CIRP Annals, 67(1), 401–404.

    Article  Google Scholar 

  7. Altintas, Y. (2001). Analytical prediction of three dimensional chatter stability in milling. JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, 44(3), 717–723.

    Google Scholar 

  8. Wei, X., Miao, E., & Ye, H. (2022). Analytical prediction of three dimensional chatter stability considering multiple parameters in milling. International Journal of Precision Engineering and Manufacturing, 23(7), 711–720.

    Article  Google Scholar 

  9. Sun, Z., Geng, D., Zheng, W., Liu, Y., Liu, L., Ying, E., Jiang, X., & Zhang, D. (2023). An innovative study on high-performance milling of carbon fiber reinforced plastic by combining ultrasonic vibration assistance and optimized tool structures. Journal of Materials Research and Technology, 22, 2131–2146.

    Article  Google Scholar 

  10. Yihang, L., ZHANG, D., Daxi, G., Zhenyu, S., Zehua, Z., Zhefei, S., JIANG, Y., & JIANG, X. (in press). Ironing effect on surface integrity and fatigue behavior during ultrasonic peening drilling of Ti-6Al-4 V. Chinese Journal of Aeronautics. https://doi.org/10.1016/j.cja.2022.12.009

  11. Yamato, S., Nakanishi, K., Suzuki, N., & Kakinuma, Y. (2021). Development of automatic chatter suppression system in parallel milling by real-time spindle speed control with observer-based chatter monitoring. International Journal of Precision Engineering and Manufacturing, 22, 227–240.

    Article  Google Scholar 

  12. Bishop, R. E. D., & Johnson, D. C. (2011). The mechanics of vibration. Cambridge University Press.

    MATH  Google Scholar 

  13. Altintas, Y. (2012). Manufacturing automation: Metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press.

    Book  Google Scholar 

  14. Kumar, U. V., & Schmitz, T. L. (2012). Spindle dynamics identification for receptance coupling substructure analysis. Precision Engineering, 36(3), 435–443.

    Article  Google Scholar 

  15. Park, S. S., Altintas, Y., & Movahhedy, M. (2003). Receptance coupling for end mills. International Journal of Machine Tools and Manufacture, 43(9), 889–896.

    Article  Google Scholar 

  16. Ertürk, A., Özgüven, H. N., & Budak, E. (2006). Analytical modeling of spindle–tool dynamics on machine tools using Timoshenko beam model and receptance coupling for the prediction of tool point FRF. International Journal of Machine Tools and Manufacture, 46(15), 1901–1912.

    Article  Google Scholar 

  17. Akbari, V. O. A., Mohammadi, Y., Kuffa, M., & Wegener, K. (2023). Identification of in-process machine tool dynamics using forced vibrations in milling process. International Journal of Mechanical Sciences, 239, 107887.

    Article  Google Scholar 

  18. Kim, S. W., Kong, J. H., Lee, S. W., & Lee, S. (2022). Recent advances of artificial intelligence in manufacturing industrial sectors: A review. International Journal of Precision Engineering and Manufacturing, 23, 1–19.

  19. Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45.

    Article  Google Scholar 

  20. Chuo, Y. S., Lee, J. W., Mun, C. H., Noh, I. W., Rezvani, S., Kim, D. C., Lee, J., Lee, S. W., & Park, S. S. (2022). Artificial intelligence enabled smart machining and machine tools. Journal of Mechanical Science and Technology, 36, 1–23.

    Article  Google Scholar 

  21. Özşahin, O., Budak, E., and Özgüven, H., "Estimation of dynamic contact parameters for machine tool spindle-holder-tool assemblies using artificial neural networks," Proc. Proceedings of the 3rd International Conference on Manufacturing Engineering (ICMEN), pp. 131–144.

  22. Cherukuri, H., Perez-Bernabeu, E., Selles, M., & Schmitz, T. (2019). Machining chatter prediction using a data learning model. Journal of Manufacturing and Materials Processing, 3(2), 45.

    Article  Google Scholar 

  23. Karandikar, J., Honeycutt, A., Schmitz, T., & Smith, S. (2020). Stability boundary and optimal operating parameter identification in milling using Bayesian learning. Journal of Manufacturing Processes, 56, 1252–1262.

    Article  Google Scholar 

  24. Byun, Y., & Baek, J.-G. (2021). Pattern classification for small-sized defects using multi-head CNN in semiconductor manufacturing. International Journal of Precision Engineering and Manufacturing, 22, 1681–1691.

    Article  Google Scholar 

  25. Surya, M. S., Prasanthi, G., Kumar, A. K., Sridhar, V., & Gugulothu, S. (2021). Optimization of cutting parameters while turning Ti-6Al-4 V using response surface methodology and machine learning technique. International Journal on Interactive Design and Manufacturing (IJIDeM), 15(4), 453–462.

    Article  Google Scholar 

  26. Moreira, L. C., Li, W., Lu, X., & Fitzpatrick, M. E. (2019). Supervision controller for real-time surface quality assurance in CNC machining using artificial intelligence. Computers & Industrial Engineering, 127, 158–168.

    Article  Google Scholar 

  27. Zhang, Y., & Xu, X. (2021). Machine learning cutting force, surface roughness, and tool life in high speed turning processes. Manufacturing Letters, 29, 84–89.

    Article  Google Scholar 

  28. Greis, N. P., Nogueira, M. L., Bhattacharya, S., Spooner, C., & Schmitz, T. (2023). Stability modeling for chatter avoidance in self-aware machining: an application of physics-guided machine learning. Journal of Intelligent Manufacturing, 34, 387–413.

  29. Schmitz, T., Cornelius, A., Karandikar, J., Tyler, C., and Smith, S., 2022, "Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability," CIRP Annals.

  30. Pawar, S., San, O., Nair, A., Rasheed, A., & Kvamsdal, T. (2021). Model fusion with physics-guided machine learning: Projection-based reduced-order modeling. Physics of Fluids, 33(6), 067123.

    Article  Google Scholar 

  31. Lim, T. W., Cabell, R., & Silcox, R. (1996). On-line identification of modal parameters using artificial neural networks. Journal of Vibration and Acoustics, 118(4), 649–656.

    Article  Google Scholar 

  32. Su, L., Huang, X., Song, M.-L., & LaFave, J. M. (2020). Automatic identification of modal parameters for structures based on an uncertainty diagram and a convolutional neural network. Structures, 28, 369–379.

    Article  Google Scholar 

  33. Wu, J. S. (2013). Analytical and numerical methods for vibration analyses. John Wiley & Sons.

    MATH  Google Scholar 

  34. Rayleigh, J. W. S., & B., L. R.,. (1945). The theory of sound. Dover publications.

    Google Scholar 

  35. Mostaghimi, H., Hassani, M., Yu, D., Hugo, R. J., and Park, S. S. (2021). Dynamic stress analysis of exposed pipes subjected to a moving in-line inspection tool. Journal of vibration and acoustics, 143(5), 051007.

  36. Chomette, B., & Mamou-Mani, A. (2018). Modal control based on direct modal parameters estimation. Journal of Vibration and Control, 24(12), 2389–2399.

    Article  Google Scholar 

  37. Chomette, B., 2023, "MIMO modal parameters identification in frequency domain,"MATLAB Central file exchange (https://www.mathworks.com/matlabcentral/fileexchange/82380-mimo-modal-parameters-identification-in-frequency-domain), Retrieved Mar 14, 2023 .

  38. MacKay, D. J. C. (1992). Bayesian interpolation. Neural Computation, 4(3), 415–447.

    Article  MATH  Google Scholar 

  39. Strang, G., 2006, Linear algebra and its applications, Belmont, CA: Thomson, Brooks/Cole.

  40. Hyndman, R. J., and Athanasopoulos, G., 2018, Forecasting: principles and practice, OTexts.

Download references

Acknowledgements

This study has been conducted with the support of the Korea Institute of Industrial Technology as "Automatic generation of machining process plan for mold parts based on artificial intelligence (KITECH JH-23-0008)".

Author information

Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4, Canada

    Hamid Mostaghimi & Simon S. Park

  2. Digital Transformation R&D Department, Korea Institute of Industrial Technology, 143 Hanggaulro, Sangrok-GuGyeonggi-do, Ansan-Si, 15588, Republic of Korea

    Dong Yoon Lee & Eunseok Nam

  3. Smart Manufacturing System R&D Department, Korea Institute of Industrial Technology, 89, Yangdaegiro-Gil, Ipjang-Myeon, Seobuk-GuChungcheongnam-do, Cheonan-Si, Republic of Korea

    Soohyun Nam

Authors
  1. Hamid Mostaghimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simon S. Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dong Yoon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Soohyun Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eunseok Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors read and approved by the final manuscript.

Corresponding author

Correspondence to Eunseok Nam.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Additional file 1

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

Mostaghimi, H., Park, S.S., Lee, D.Y. et al. Prediction of Tool Tip Dynamics Through Machine Learning and Inverse Receptance Coupling. Int. J. Precis. Eng. Manuf. 24, 1739–1752 (2023). https://doi.org/10.1007/s12541-023-00831-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-023-00831-6

Keywords

Search

Navigation