Skip to main content
SpringerLink
Log in

Online identification of milling forces using acceleration signals

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

Abstract

Milling force is one of the key output variables related to milling efficiency and accuracy. However, the time-consuming identification of milling forces cannot be evaluated online for machining performance during the milling process. In this paper, a novel millisecond-level method is developed to identify the milling forces online using acceleration signals. Firstly, the milling force identification problem is transformed into an ill-posed problem in the time-domain convolution framework. Next, the advantages of the regularization method for the ill-posed problem are analyzed. Then, the properties of the transfer matrix and the regularization operator are used to reduce the milling force identification time. Lastly, the proposed method is verified with several sets of milling experiments, and the results show that the accuracy of milling force identification is acceptable and the identification time is less than 10 ms.

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

Similar content being viewed by others

Availability of data and material

It can be obtained from the author by mail.

Code availability

Not applicable

References

  1. Abele E, Altintas Y, Brecher C (2010) Machine tool spindle units. CIRP Annals - Manufacturing Technology. 59(2):781–802. https://doi.org/10.1016/j.cirp.2010.05.002

    Article  Google Scholar 

  2. Cao HR, Zhang XW, Chen XF (2017) The concept and progress of intelligent spindles: A review. International Journal of Machine Tools and Manufacture. 112:21–52. https://doi.org/10.1016/j.ijmachtools.2016.10.005

    Article  Google Scholar 

  3. Li DH, Cao HR, Chen XF (2022) Displacement difference feedback control of chatter in milling processes. The International Journal of Advanced Manufacturing Technology. 120:6053–6066. https://doi.org/10.1007/s00170-022-09128-w

    Article  Google Scholar 

  4. Martella M, Rotating dynamometer. (Product News). (Kistler Instrument’s Type 9125), Sensors Magazine. (2002)

  5. Li X, Djordjevich A, Venuvinod PK (2000) Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring. IEEE Transactions on Industrial Electronics. 47(3):697–702. https://doi.org/10.1109/41.847910

    Article  Google Scholar 

  6. Li X, Venuvinod PK, Chen MK (2000) Feed Cutting Force Estimation from the Current Measurement with Hybrid Learning, The International Journal of. Advanced Manufacturing. 16:859–862. https://doi.org/10.1007/s001700070002

    Article  Google Scholar 

  7. Li XL, Li HX, Guan XP, Du R (2004) Fuzzy estimation of feed-cutting force from current measurement-a case study on intelligent tool wear condition monitoring, IEEE Transactions on Systems, Man, and Cybernetics. Part C (Applications and Reviews). 34(4):506–512. https://doi.org/10.1109/TSMCC.2004.829296

    Article  Google Scholar 

  8. Jeong YH, Cho DW (2002) Estimating cutting force from rotating and stationary feed motor currents on a milling machine. International Journal of Machine Tools and Manufacture. 42(14):1559–1566. https://doi.org/10.1016/S0890-6955(02)00082-2

    Article  Google Scholar 

  9. Altintas Y (1992) Prediction of cutting forces and tool breakage in milling from feed drive current measurements. Transactions of the ASME Journal of Engineering for Industry. 114(4):386–392. https://doi.org/10.1115/1.2900688

  10. Aslan D, Altintas Y (2018) Prediction of cutting forces in five-axis milling using feed drive current measurements. IEEE/ASME Transactions on Mechatronics. 23(2):833–844. https://doi.org/10.1109/TMECH.2018.2804859

    Article  Google Scholar 

  11. Kim D, Jeon D (2011) Fuzzy-logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors. Precision Engineering. 35(1):143–152. https://doi.org/10.1016/j.precisioneng.2010.09.001

    Article  Google Scholar 

  12. Zhu JM, Wang J, Zhang TC, Li XR, Dynamic milling force measuring method based on cutting tool vibration displacement, Chinese Journal of Scientific Instrument. 35(12) (2014) 2772–2782, https://doi.org/10.19650/j.cnki.cjsi.2014.12.016

  13. Albrecht A, Park SS, Altintas Y, Pritschow G (2005) High frequency bandwidth cutting force measurement in milling using capacitance displacement sensors. International Journal of Machine Tools and Manufacture. 45(9):993–1008. https://doi.org/10.1016/j.ijmachtools.2004.11.028

    Article  Google Scholar 

  14. Wan M, Yuan H, Feng J, Zhang WH, Yin W (2017) Industry-oriented method for measuring the cutting forces based on the deflections of tool shank. International Journal of Mechanical Sciences. 130:315–323. https://doi.org/10.1016/j.ijmecsci.2017.06.022

    Article  Google Scholar 

  15. Gregory WV, Dominique AR, Feng J, Gregory MC (2022) Real-time estimation of cutting forces via physics-inspired data-driven model. CIRP Annals - Manufacturing Technology. 71:317–320. https://doi.org/10.1016/j.cirp.2022.04.071

    Article  Google Scholar 

  16. Wang CX, Zhang XW, Qiao BJ, Chen XF, Cao HR (2018) Milling force identification from acceleration signals using regularization method based on TSVD in peripheral milling. Procedia CIRP. 77:18–21. https://doi.org/10.1016/j.procir.2018.08.195

    Article  Google Scholar 

  17. Zhou J, Mao XY, Liu HQ, Li B, Peng YL (2018) Prediction of cutting force in milling process using vibration signals of machine tool. The International Journal of Advanced Manufacturing Technology. 99:965–984. https://doi.org/10.1007/s00170-018-2464-1

    Article  Google Scholar 

  18. Salehi M, Albertelli P, Goletti M, Ripamonti F, Tomasini G, Monno M (2015) Indirect model based estimation of cutting force and tool tip vibrational behavior in milling machines by sensor fusion. Procedia CIRP. 33(1):239–244. https://doi.org/10.1016/j.procir.2015.06.043

    Article  Google Scholar 

  19. Mostaghimi H, Park CI, Kang G, Park CC, Dong YL (2021) Reconstruction of cutting forces through fusion of accelerometer and spindle current signals. Journal of Manufacturing Processes. 68:990–1003. https://doi.org/10.1016/j.jmapro.2021.06.007

    Article  Google Scholar 

  20. Wang CX, Zhang XW, Qiao BJ, Chen XF (2019) Dynamic force identification in peripheral milling based on CGLS using filtered acceleration signals and averaged transfer functions. Journal of Manufacturing Science and Engineering. 141(6):1–8. https://doi.org/10.1115/1.4043362

    Article  Google Scholar 

  21. Wan M, Yin W, Zhang WH, Liu H (2017) Improved inverse filter for the correction of distorted measured cutting forces. International Journal of Mechanical Sciences. 120:276–285. https://doi.org/10.1016/j.ijmecsci.2016.11.033

    Article  Google Scholar 

  22. Golub GH, Reinsch C (1970) Singular value decomposition and least squares solutions. Numerische Mathematik. 14:403–420. https://doi.org/10.1007/BF02163027

    Article  MathSciNet  MATH  Google Scholar 

  23. Bishop CM (1995) Training with Noise is Equivalent to Tikhonov Regularization. Neural Computation. 7(1):108–116. https://doi.org/10.1162/neco.1995.7.1.108

    Article  Google Scholar 

  24. Roozbeh M, Arashi M, Hamzah NA (2020) Generalized Cross-Validation for Simultaneous Optimization of Tuning Parameters in Ridge Regression, Iranian Journal of Science and Technology, Transactions A. Science. 44:473–485. https://doi.org/10.1007/s40995-020-00851-1

  25. Akgun MA, Garcelon JH, Haftka TR (2001) Fast exact linear and non-linear structural reanalysis and the Sherman-Morrison-Woodbury formulas. International Journal for Numerical Methods in Engineering. 50(7):1587–1606. https://doi.org/10.1002/nme.87

    Article  MATH  Google Scholar 

  26. Böttcher A, Silbermann B (1999) Introduction to Large Truncated Toeplitz Matrices, Springer. New York. https://doi.org/10.1007/978-1-4612-1426-7

Download references

Funding

This work is supported by the National Natural Science Foundation of China (No. 51922084).

Author information

Authors and Affiliations

  1. National Key Lab of Aerospace Power System and Plasma Technology, Xi’an Jiaotong University, Xi’an, 710049, China

    Qi Li, Maxiao Hou & Hongrui Cao

Authors
  1. Qi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maxiao Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongrui Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Qi Li: resources, methodology, software. Maxiao Hou: conceptualization, writing — original draft, software. Hongrui Cao: conceptualization, writing — review and editing, supervision, project administration, funding acquisition.

Corresponding author

Correspondence to Hongrui Cao.

Ethics declarations

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

Not applicable

Conflict of interest

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 (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

Li, Q., Hou, M. & Cao, H. Online identification of milling forces using acceleration signals. Int J Adv Manuf Technol 127, 4491–4501 (2023). https://doi.org/10.1007/s00170-023-11645-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-11645-1

Keywords

Search

Navigation