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A novel process damping identification model and cutting stability prediction

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Abstract

This paper presents an analytical model for identifying process damping caused by interference between tool and workpiece during the cutting process. In contrast to the traditional discrete method, the model identifies the interference area between tool and workpiece using a complete analytical method. The obtained interference area can achieve the accuracy of the result obtained by using a shorter discrete step, but the time required for the model is shorter than that of the discrete method and is unaffected by the discrete step. The influence of tool wear band on the process damping coefficient is also considered. The process damping coefficient is identified based on the interference area obtained by the complete analytical model, and the damping coefficient of the non-linear process is linearized by the energy method. Finally, the cutting stability lobe diagram is drawn considering the influence of process damping, and the cutting stability region is predicted. Compared with the lobe diagram without process damping, it is found that process damping can significantly improve the cutting stability in low-speed cutting, and a series of cutting tests are carried out to verify the correctness of the prediction model.

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References

  1. Altintas Y, Stepan G, Budak E, Schmitz T, Kilic Z (2020) Chatter stability of machining operations. J Manuf Sci E-T Asme 142(11):110801

    Article  Google Scholar 

  2. Vnukov Y, Germashev A, Logominov V, Kryshtal V (2017) Possibilities of using stability lobe diagram for stability prediction of high speed milling of thinwalled details. Mech Adv T 1(79):1–8

    Google Scholar 

  3. Brecher C, Chavan P, Epple A (2108) Efficient determination of stability lobe diagrams by in-process varying of spindle speed and cutting depth. Adv Manuf 6(3): 272-279

  4. Yue C, Gao H, Liu X, Liang S, Wang L (2019) A review of chatter vibration research in milling. Chinese J Aeronaut 32(2):215–242

    Article  Google Scholar 

  5. Shi Z, Liu L, Liu Z, Zhang X (2017) Frequency-domain stability lobe prediction for high-speed face milling process under tool–workpiece dynamic interaction. P I Mech Eng B-J Eng 231(13):2336–2346

    Google Scholar 

  6. Lu K, Lian Z, Gu F, Liu H (2018) Model-based chatter stability prediction and detection for the turning of a flexible workpiece. Mech Syst Signal Pr 100:814–826

    Article  Google Scholar 

  7. Singh K, Singh R (2018) Chatter stability prediction in high-speed micromilling of Ti6Al4V via finite element based microend mill dynamics. Adv Manuf 6(1):95–106

    Article  Google Scholar 

  8. Dai Y, Li H, Hao B (2018) An improved full-discretization method for chatter stability prediction. Int J Adv Manuf Tech 96(9):3503–3510

    Article  Google Scholar 

  9. Zhai Y, Gao H, Wang Y, Li R (2019) Influence of cutting parameters on force coefficients and stability in plunge milling. Int J Adv Manuf Tech 104(5):2513–2523

    Article  Google Scholar 

  10. Wan M, Gao TQ, Feng J, Zhang W (2019) On improving chatter stability of thin-wall milling by prestressing. J Mater Process Tech 264:32–44

    Article  Google Scholar 

  11. Yue C, Liu X, Liang S (2017) A model for predicting chatter stability considering contact characteristic between milling cutter and workpiece. Int J Adv Manuf Tech 88(5–8):2345–2354

    Article  Google Scholar 

  12. Li Z, Jiang S, Sun Y (2019) Chatter stability and surface location error predictions in milling with mode coupling and process damping. P I Mech Eng B-J Eng 233(3):686–698

    Google Scholar 

  13. Moradi H, Vossoughi G, Movahhedy M, Ahmadian M (2013) Forced vibration analysis of the milling process with structural nonlinearity, internal resonance, tool wear and process damping effects. Int J Nonlin Mech 54:22–34

    Article  Google Scholar 

  14. Altintas Y, Eynian M, Onozuka H (2008) Identification of dynamic cutting force coefficients and chatter stability with process damping. Cirp Ann-Manuf Techn 57(1):371–374

    Article  Google Scholar 

  15. Ahmadi K, Ismail F (2010) Experimental investigation of process damping nonlinearity in machining chatter. Int J Mach Tool Manu 50(11):1006–1014

    Article  Google Scholar 

  16. Ahmadi K, Ismail F (2011) Analytical stability lobes including nonlinear process damping effect on machining chatter. Int J Mach Tool Manu 51(4):296–308

    Article  Google Scholar 

  17. Tang X, Peng F, Yan R, Zhua Z, Li Z, Xin S (2021) Nonlinear process damping identification using finite amplitude stability and the influence analysis on five-axis milling stability. Int J Mech Sci 190:106008

    Article  Google Scholar 

  18. Wang J, Uhlmann E, Oberschmidt D, Sung C, Perfilov I (2016) Critical depth of cut and asymptotic spindle speed for chatter in micro milling with process damping. Cirp Ann-Manuf Techn 65(1):113–116

    Article  Google Scholar 

  19. Jin X, Altintas Y (2013) Chatter stability model of micro-milling with process damping. J Manuf Sci E-T Asme 135(3):031011

    Article  Google Scholar 

  20. Budak E, Tunc LT (2009) A new method for identification and modeling of process damping in machining. J Manuf Sci E-T Asme 131(5):051019

    Article  Google Scholar 

  21. Schmitz TL, Smith KS (2009) Machining dynamics. Springer, Berlin

    Book  Google Scholar 

Download references

Funding

This research was funded by the National Natural Science Foundation of China Grant No. 51875005 and 51475010.

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Authors and Affiliations

  1. Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China

    Dongju Chen, Xuan Zhang, Shupei Li, Ri Pan, Kun Sun & Jinwei Fan

  2. Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing, 100124, China

    Dongju Chen, Xuan Zhang, Shupei Li, Ri Pan, Kun Sun & Jinwei Fan

Authors
  1. Dongju Chen
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  2. Xuan Zhang
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  3. Shupei Li
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  4. Ri Pan
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  5. Kun Sun
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  6. Jinwei Fan
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Contributions

All authors contributed to the study conception and design. Dongju Chen: conceptualization, methodology, resources, funding acquisition, and writing—review and editing. Xuan Zhang: investigation, validation, visualization, and writing—original draft. Shupei Li: methodology, software, visualization, and writing—review and editing. Ri Pan: formal analysis, software, and supervision. Kun Sun: formal analysis, investigation, and data curation. Jinwei Fan: project administration, writing—original draft, and supervision. All authors read and approved the final manuscript.

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Correspondence to Dongju Chen.

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Chen, D., Zhang, X., Li, S. et al. A novel process damping identification model and cutting stability prediction. Int J Adv Manuf Technol 126, 4573–4579 (2023). https://doi.org/10.1007/s00170-023-11428-8

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  • DOI: https://doi.org/10.1007/s00170-023-11428-8

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