Identification of dynamics for machining systems

Authors

DOI:

https://doi.org/10.20535/2521-1943.2024.8.4(103).305837

Keywords:

machining system, identification of dynamic parameters, experimental modal analysis

Abstract

Cutting processes are carried out in an elastic machining system, which is multi-mass with negative and positive loop control with a delay in construed mathematical models. Its behavior during the cutting process is entirely determined by dynamic properties and an adequate parameters of mathematical model is necessary to control the process. The paper proposes a method for identifying such dynamic parameters of the machining system, which include natural vibration frequencies, vibration damping coefficients, and stiffness of the replacement model of single-mass system in the direction of the machine-CNC coordinate axes.

It is proposed to identify such parameters as a result of experimental modal analysis by impacting the elements of the tool and workpiece with an impact hammer and processing the impulse signal with a fast Fourier transform. It is proposed to adapt the results obtained to the adopted mathematical model of the machining system, presented in the form of two masses, each with two degrees of freedom, according to the equivalence of the spectrum signal power or its spectral density. The cutting force model in the form of a linearized dependence on the area of undeformed chips needs to be clarified by the coefficient using experimental oscillograms obtained during milling of a workpiece mounted on a dynamometer table. Based on the identified parameters of the machining system, a stability diagram was constructed in the “spindle speed – feed” coordinates and experiments were carried out under conditions in the zone of stable and unstable cutting. Evaluation of the roughness of the machined surface confirmed the correspondence to the location of the stability lobes diagram constructed using the identified parameters, which indicates the effectiveness of the proposed identification method.

References

  1. O. B. Adetoro, W. M. Sim, P. H. Wen, “An improved prediction of stability lobes using nonlinear thin wall dynamics”, Jour-nal of Materials Processing Technology, Vol. 210, pp. 969–979, 2010. https://doi.org/10.1016/j.jmatprotec.2010.02.009.
  2. K. Cheng, “Machining Dynamics. Fundamentals, Applications and Practices”, Springer-Verlag London Limited, 2009. http://doi.org/10.1007/978-1-84628-368-0.
  3. L. Tony, K. Schmitz, S. Smith, “Machining Dynamics. Frequency Response to Improved Productivity”, Springer Internation-al Publishing AG, part of Springer Nature 2019. https://doi.org/10.1007/978-3-319-93707-6.
  4. D. J. Ewins, “Modal Testing: Theory, Practice and Application. Published by Research Studies Press”, 2009. https://books.google.co.uk/books/about/Modal_Testing.html?id=Z4QoAQAAMAAJ&redir_esc=y.
  5. J. Berthold et al., “Identification of natural frequencies of machine tools during milling: comparison of the experimental modal analysis and the operational modal analysis”, Production Engineering, 2024. https://doi.org/10.1007/s11740-024-01270-6.
  6. Hui Cai et al., “Estimation of FRFs of machine tools in output-only modal analysis”, The International Journal of Advanced Manufacturing Technology, Vol. 77, pp. 117–130, 2015. https://doi.org/10.1007/s00170-014-6439-6.
  7. Yen-Po Liu, Y. Altintas, “In-process identification of machine tool dynamics”, CIRP Journal of Manufacturing Science and Technology, Vol. 32, pp. 322–337, 2021. https://doi.org/10.1016/j.cirpj.2021.01.007.
  8. J. Ellinger, M. F. Zaeh, “Automated Identification of Linear Machine Tool Model Parameters Using Global Sensitivity Anal-ysis”, Machines, Vol. 10, 535, 2022. https://doi.org/10.3390/machines10070535.
  9. R. Isermann, M. Munchhof, “Identification of Dynamic Systems An Introduction with Applications”, Springer Heidelberg Dordrecht London New York 2011. https://doi.org/10.1007/978-3-540-78879-9.
  10. N. Dabiri et al., “A unified FFT-based mechanistic force coefficient identification model for isotropic and anisotropic materi-als”, CIRP Journal of Manufacturing Science and Technology, Vol. 49(2), pp, 216–229, 2024. https://doi.org/10.1016/j.cirpj.2024.01.009.
  11. Y. Altintas, “Virtual High Performance Machining”, Procedia CIRP, Vol. 46, pp. 372–378, 2016. https://doi.org/10.1016/j.procir.2016.04.154.
  12. R. Ward et al., “Machining Digital Twin using real-time model-based simulations and lookahead function for closed loop ma-chining control”, The International Journal of Advanced Manufacturing Technology, Vol. 117, pp. 3615–3629, 2021. https://doi.org/10.1007/s00170-021-07867-w.
  13. E. Kushnir, “Determination of Machine Tool Frequency Response Function During Cutting”, Copyright 2004 by ASME. https://doi.org/10.1115/IMECE2004-59573.
  14. A. Archenti et al., “A new method for circular testing of machine tools under loaded condition”, 5th CIRP Conference on High Performance Cutting 2012, 2212–8271. https://dx.doi.org/10.1016/j.procir.2012.04.102.
  15. R. P. Agarwal, L. Berezansky, E. Braverman, A. Domoshnitsky, Introduction to Oscillation Theory. In: Nonoscillation Theo-ry of Functional Differential Equations with Applications, Springer, New York, NY, pp. 1–12, 2012. https://doi.org/10.1007/978-1-4614-3455-9_1.
  16. Machining handbook 2-part EN. https://www.hoffmann-group.com/GB/en/houk/know-how/machining-handbook/e/61245/.
  17. Y. V. Petrakov, O. A. Ohrimenko, M. O. Sikailo, A. V. Myhovych, “Cutting forces simulation for end milling”, Journal of Engineering Sciences (Ukraine), Vol. 10(2), pp. A27–A33, 2023. https://doi.org/10.21272/jes.2023.10(2).a4.
  18. Y. Petrakov, O. Ohrimenko, and M. Sikailo, “Ensuring the stability of machining when using end mills”, EEJET, Vol. 5, No. 1 (125), pp. 73–80, 2023. https://doi.org/10.15587/1729-4061.2023.287009.

Downloads

Published

2024-12-26

How to Cite

[1]
Y. Petrakov, O. Okhrimenko, and M. Sikailo, “Identification of dynamics for machining systems”, Mech. Adv. Technol., vol. 8, no. 4(103), pp. 337–345, Dec. 2024.

Issue

Vol. 8 No. 4(103) (2024)

Section

Up-to-date machines and the technologies of mechanical engineering

License

Copyright (c) 2024 Юрій Петраков, Олександр Охріменко, Максим Сікайло

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The ownership of copyright remains with the Authors.
 
Authors may use their own material in other publications provided that the Journal is acknowledged as the original place of publication and National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” as the Publisher.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under CC BY 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work