Skip to main content
SpringerLink
Log in

Digital modeling-driven chatter suppression for thin-walled part manufacturing

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Thin-walled parts are widely used in various industries such as aerospace and automotive, but the manufacturing processes are often harmed by chatter which is a self-excited vibration because of the poor rigidity in the direction perpendicular to the wall surface. The traditional stability lobe diagram (SLD) method can predict chatter based on the manufacturing system and workpiece parameters. However, these parameters could vary along with the manufacturing execution, compromising SLD's accuracy and even feasibility. To enable effective chatter suppression in thin-walled part milling, this study proposes a digital twin model, where two sub-models including the cutting parameters optimization and chatter detection are established. In the sub-model of cutting parameters optimization, a real-time SLD considering the time-varying modal parameters at the cutting region of the workpiece is generated as the optimization criteria. The sub-model of chatter detection can recognize chatter by a fusional analysis of the multiple sensors' signals, including vibration, force, and sound. Considering the bias of real-time SLD, these two sub-models are combined to output optimized cutting parameters to avoid chatter. Besides, a monitoring window to visualize the milling scenario and a database to record the manufacturing data are implemented in the digital twin model. According to the milling experiments, the digital twin model is validated to perform more effectively in chatter suppression than the traditional stationary SLD method.

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
Fig. 16

Similar content being viewed by others

Abbreviations

EMD:

Empirical mode decomposition

FDM:

Full-discretization method

FEM:

Finite element method

FRF:

Frequency response function

IRCSA:

Inverse receptance coupling substructure analysis

NC:

Numerical control

RCSA:

Receptance coupling substructure analysis

SDM:

Semi-discretization method

SLD:

Stability lobe diagram

SVM:

Support vector machine

VMD:

Variational mode decomposition

References

  • Altintas, Y., & Budak, E. (1995). Analytical prediction of stability lobes in milling. CIRP Annals: Manufacturing Technology, 44(1), 357–362.

    Article  Google Scholar 

  • Astarloa, A., et al. (2022). Damping in ram based vertical lathes and portal machines. CIRP Annals, 71(1), 369–372.

    Article  Google Scholar 

  • Bielefeldt, B., Hochhalter, J., & Hartl, D. (2015). Computationally efficient analysis of SMA sensory particles embedded in complex aerostructures using a substructure approach. In Smart materials, adaptive structures and intelligent systems. American Society of Mechanical Engineers.

  • Bravo, U., et al. (2005). Stability limits of milling considering the flexibility of the workpiece and the machine. International Journal of Machine Tools and Manufacture, 45(15), 1669–1680.

    Article  Google Scholar 

  • Budak, E., & Altintas, Y. (1998). Analytical prediction of chatter stability in milling—Part I: General formulation. Journal of Dynamic Systems, Measurement, and Control, 120(1), 22–30.

    Article  Google Scholar 

  • Butcher, E. A., et al. (2009). Analysis of milling stability by the Chebyshev collocation method: Algorithm and optimal stable immersion levels. Journal of Computational and Nonlinear Dynamics, 4(3), 031003.

    Article  Google Scholar 

  • Campa, F., De Lacalle, L. L., & Celaya, A. (2011). Chatter avoidance in the milling of thin floors with bull-nose end mills: Model and stability diagrams. International Journal of Machine Tools and Manufacture, 51(1), 43–53.

    Article  Google Scholar 

  • Campa, F. J., et al. (2007). Selection of cutting conditions for a stable milling of flexible parts with bull-nose end mills. Journal of Materials Processing Technology, 191(1–3), 279–282.

    Article  Google Scholar 

  • Cao, H., Lei, Y., & He, Z. (2013). Chatter identification in end milling process using wavelet packets and Hilbert-Huang transform. International Journal of Machine Tools and Manufacture, 69, 11–19.

    Article  Google Scholar 

  • Cao, H., Li, B., & He, Z. (2012). Chatter stability of milling with speed-varying dynamics of spindles. International Journal of Machine Tools and Manufacture, 52(1), 50–58.

    Article  Google Scholar 

  • Ding, Y., et al. (2010a). A full-discretization method for prediction of milling stability. International Journal of Machine Tools and Manufacture, 50(5), 502–509.

    Article  Google Scholar 

  • Ding, Y., et al. (2010b). Second-order full-discretization method for milling stability prediction. International Journal of Machine Tools and Manufacture, 50(10), 926–932.

    Article  Google Scholar 

  • Ding, Y., et al. (2011). Numerical integration method for prediction of milling stability. Journal of Manufacturing Science and Engineering, 133(3), 031005.

    Article  Google Scholar 

  • Ealo, J., et al. (2018). A practical study of joints in three-dimensional Inverse Receptance Coupling Substructure Analysis method in a horizontal milling machine. International Journal of Machine Tools and Manufacture, 128, 41–51.

    Article  Google Scholar 

  • El-Dib, Y. (2018). Stability analysis of a strongly displacement time-delayed Duffing oscillator using multiple scales homotopy perturbation method. Journal of Applied and Computational Mechanics, 4(4), 260–274.

    Google Scholar 

  • Feng, J., et al. (2018). Mechanism of process damping in milling of thin-walled workpiece. International Journal of Machine Tools and Manufacture, 134, 1–19.

    Article  Google Scholar 

  • Ghosh, A. K., et al. (2020). Machining phenomenon twin construction for industry 4.0: A case of surface roughness. Journal of Manufacturing and Materials Processing, 4(1), 11.

    Article  Google Scholar 

  • Gonzalez, H., et al. (2021). Flank-milling of integral blade rotors made in Ti6Al4V using Cryo CO2 and minimum quantity lubrication. Journal of Manufacturing Science and Engineering, 143(9), 091011.

    Article  Google Scholar 

  • Insperger, T., & Stépán, G. (2002). Semi-discretization method for delayed systems. International Journal for Numerical Methods in Engineering, 55, 503–518.

    Article  Google Scholar 

  • Insperger, T., & Stépán, G. (2004). Updated semi-discretization method for periodic delay-differential equations with discrete delay. International Journal for Numerical Methods in Engineering, 61(1), 117–141.

    Article  Google Scholar 

  • Ji, Y., et al. (2018). Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation. Journal of Sound and Vibration, 433, 138–159.

    Article  Google Scholar 

  • Jin, X., et al. (2015). 3D stability lobe considering the helix angle effect in thin-wall milling. The International Journal of Advanced Manufacturing Technology, 82(9–12), 2123–2136.

    Google Scholar 

  • Kolluru, K., & Axinte, D. (2014). Novel ancillary device for minimising machining vibrations in thin wall assemblies. International Journal of Machine Tools and Manufacture, 85, 79–86.

    Article  Google Scholar 

  • Ladj, A., et al. (2021). A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective. Journal of Manufacturing Systems, 58, 168–179.

    Article  Google Scholar 

  • Li, X., et al. (2021). Active suppression of milling chatter with LMI-based robust controller and electromagnetic actuator. Journal of Materials Processing Technology, 297, 117238.

    Article  Google Scholar 

  • Liu, M., et al. (2021). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346–361.

    Article  Google Scholar 

  • Mancisidor, I., et al. (2014). Receptance coupling for tool point dynamic prediction by fixed boundaries approach. International Journal of Machine Tools and Manufacture, 78, 18–29.

    Article  Google Scholar 

  • Mane, I., et al. (2008). Stability-based spindle speed control during flexible workpiece high-speed milling. International Journal of Machine Tools and Manufacture, 48(2), 184–194.

    Article  Google Scholar 

  • Mykoniatis, K., & Harris, G. A. (2021). A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach. Journal of Intelligent Manufacturing, 32(7), 1899–1911.

    Article  Google Scholar 

  • Olvera, D., et al. (2014). Determination of the stability lobes in milling operations based on homotopy and simulated annealing techniques. Mechatronics, 24(3), 177–185.

    Article  Google Scholar 

  • Schmitz, T. L., & Donalson, R. R. (2000). Predicting high-speed machining dynamics by substructure analysis. CIRP Annals, 49(1), 303–308.

    Article  Google Scholar 

  • Shafto, M., et al. (2012). Modeling, simulation, information technology and processing roadmap (Vol. 32, pp. 1–38). National Aeronautics and Space Administration.

  • Smith, S., et al. (2012). Sacrificial structure preforms for thin part machining. CIRP Annals, 61(1), 379–382.

    Article  Google Scholar 

  • Sun, Y., & Jiang, S. (2018). Predictive modeling of chatter stability considering force-induced deformation effect in milling thin-walled parts. International Journal of Machine Tools and Manufacture, 135, 38–52.

    Article  Google Scholar 

  • Tao, J., et al. (2020). Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method. Journal of Intelligent Manufacturing, 31(5), 1243–1255.

    Article  Google Scholar 

  • Tobias, S., & Fishwick, W. (1958). Theory of regenerative machine tool chatter. The Engineer, 205(7), 199–203.

    Google Scholar 

  • Unver, H. O., & Sener, B. (2021). A novel transfer learning framework for chatter detection using convolutional neural networks. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01839-3

    Article  Google Scholar 

  • Urbikain, G., et al. (2012). Stability prediction in straight turning of a flexible workpiece by collocation method. International Journal of Machine Tools and Manufacture, 54, 73–81.

    Article  Google Scholar 

  • Urbikain, G., et al. (2015). Preventing chatter vibrations in heavy-duty turning operations in large horizontal lathes. Journal of Sound and Vibration, 340, 317–330.

    Article  Google Scholar 

  • Wan, M., et al. (2022). Chatter suppression in the milling process of the weakly-rigid workpiece through a moving fixture. Journal of Materials Processing Technology, 299, 117293.

    Article  Google Scholar 

  • Wang, D., et al. (2019a). Milling stability analysis with considering process damping and mode shapes of in-process thin-walled workpiece. International Journal of Mechanical Sciences, 159, 382–397.

    Article  Google Scholar 

  • Wang, R., et al. (2022). Multi-condition identification in milling Ti–6Al–4V thin-walled parts based on sensor fusion. Mechanical Systems and Signal Processing, 164, 108264.

    Article  Google Scholar 

  • Wang, S., Song, Q., & Liu, Z. (2019b). Vibration suppression of thin-walled workpiece milling using a time–space varying PD control method via piezoelectric actuator. The International Journal of Advanced Manufacturing Technology, 105(7–8), 2843–2856.

    Article  Google Scholar 

  • Wang, Y., et al. (2021). A kMap optimized VMD–SVM model for milling chatter detection with an industrial robot. Journal of Intelligent Manufacturing, 33, 1–20.

    Google Scholar 

  • Yang, Y., Dai, W., & Liu, Q. (2015). Design and implementation of two-degree-of-freedom tuned mass damper in milling vibration mitigation. Journal of Sound and Vibration, 335, 78–88.

    Article  Google Scholar 

  • Yang, Y., et al. (2016). Chatter prediction for the peripheral milling of thin-walled workpieces with curved surfaces. International Journal of Machine Tools and Manufacture, 109, 36–48.

    Article  Google Scholar 

  • Zakrajsek, A. J., & Mall, S. (2017). The development and use of a digital twin model for tire touchdown health monitoring. In 58th AIAA/ASCE/AHS/ASC structures, structural dynamics, and materials conference, 2017.

  • Zeng, S., et al. (2012). A novel approach to fixture design on suppressing machining vibration of flexible workpiece. International Journal of Machine Tools and Manufacture, 58, 29–43.

    Article  Google Scholar 

  • Zhang, H., et al. (2017). A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access, 5, 26901–26911.

    Article  Google Scholar 

  • Zhang, Z., et al. (2015). A novel approach for the prediction of the milling stability based on the Simpson method. International Journal of Machine Tools and Manufacture, 99, 43–47.

    Article  Google Scholar 

  • Zhang, Z., et al. (2018). Chatter mitigation for the milling of thin-walled workpiece. International Journal of Mechanical Sciences, 138–139, 262–271.

    Article  Google Scholar 

  • Zhou, K., et al. (2017). High-order full-discretization methods for milling stability prediction by interpolating the delay term of time-delayed differential equations. The International Journal of Advanced Manufacturing Technology, 93(5), 2201–2214.

    Article  Google Scholar 

  • Zhu, Z., et al. (2021). Digital Twin-driven machining process for thin-walled part manufacturing. Journal of Manufacturing Systems, 59, 453–466.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by National Natural Science Foundation of China under Grant No. 51875311, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2020A1515011199, and Shenzhen Fundamental Research Funds under Grant No. WDZC20200817152115001.

Author information

Author notes

  1. Guo Zhou and Kai Zhou have contributed equally to this work.

Authors and Affiliations

  1. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518055, China

    Guo Zhou & Xiaohao Wang

  2. Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China

    Kai Zhou, Jing Zhang, Meng Yuan, Xiaohao Wang, Pingfa Feng, Min Zhang & Feng Feng

  3. Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Pingfa Feng

Authors
  1. Guo Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meng Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaohao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pingfa Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Min Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Feng Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Min Zhang or Feng Feng.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Zhou, G., Zhou, K., Zhang, J. et al. Digital modeling-driven chatter suppression for thin-walled part manufacturing. J Intell Manuf 35, 289–305 (2024). https://doi.org/10.1007/s10845-022-02045-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-022-02045-5

Keywords

Search

Navigation