Predicting milling force variation in time and space domain for multi-toothed face milling

Abstract

Variations of milling force in time domain and space domain are the common behaviors of face milling for large-scale component with discontinuous multi-holed surface, which specifically generate surface variations along feed direction and circumferential direction. An accurate prediction model of local milling force is a key issue to reliable simulation of the milling force variations and the resultant machined surface variations. However, milling force variation modeling is still a significant challenge due to changing of instantaneous chip thickness in angular domain and the non-constant cutting coefficients under different milling conditions. Therefore, a general milling force model with cutting coefficients derived from average forces of milling experiments may loss the robustness when applied to different milling conditions. To address this issue, this paper attempts to provide a methodology to identify the unknown cutting coefficients in dual-mechanism local force model for face milling through the periodic averages integrated with multiple angular local forces which are measured at specific sampling angular positions in each single test. And each of the dual-mechanism cutting coefficients is modeled as multi-quadratic regression equation individually based on response surface methodology (RSM) via the design of experiments. The influence of feed per tooth, spindle speed, and cutting depth on the cutting coefficients are studied in order to build a model which can predict reliably the local milling force for different process parameter combinations. The proposed prediction model of milling force variations in time and space domains is verified by experimental measurements.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. 1.

    Nguyen HT, Wang H, Tai BL, Ren J, Jack Hu S, Shih A (2015) High-definition metrology enabled surface variation control by cutting load balancing. J Manuf Sci E Trans ASME 138(2):21010

    Article  Google Scholar 

  2. 2.

    Liu S, Jin S, Zhang X, Chen K, Tian A, Xi L (2019) A coupled model for the prediction of surface variation in face milling large-scale workpiece with complex geometry. J Manuf Sci E Trans ASME 141(3):31009

    Article  Google Scholar 

  3. 3.

    ELK S, Erdim H, Lazoglu I (2012) Offline force control and feedrate scheduling for complex free form surfaces in 5-Axis milling. Procedia Cirp 1(1):96–101

    Google Scholar 

  4. 4.

    Nguyen HT, Wang H, Hu SJ (2013) Characterization of cutting force induced surface shape variation in face milling using high-definition metrology. J Manuf Sci E Trans ASME 135(4):41014

    Article  Google Scholar 

  5. 5.

    Yang Y, Liu Q, Zhang B (2014) Three-dimensional chatter stability prediction of milling based on the linear and exponential cutting force model. Int J Adv Manuf Technol 72(9–12):1175–1185

    Article  Google Scholar 

  6. 6.

    Tandon V, El-Mounayri H (2001) A novel artificial neural networks force model for end milling. Int J Adv Manuf Technol 18(10):693–700

    Article  Google Scholar 

  7. 7.

    Ghorbani H, Moetakef-Imani B (2016) Specific cutting force and cutting condition interaction modeling for round insert face milling operation. Int J Adv Manuf Technol 84(5):1705–1715

    Google Scholar 

  8. 8.

    Radhakrishnan T, Nandan U (2005) Milling force prediction using regression and neural networks. J Intell Manuf 16(1):93–102

    Article  Google Scholar 

  9. 9.

    Szecsi T (1999) Cutting force modeling using artificial neural networks. J Mater Process Technol 92-93(3):344–349

    Article  Google Scholar 

  10. 10.

    Aykut Ş, Gölcü M, Semiz S, Ergür HS (2007) Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network. J Mater Process Technol 190(1):199–203

    Article  Google Scholar 

  11. 11.

    Ehmann KKSD (1997) Machining process modeling: a review. J Manuf Sci E Trans ASME 119(11):655–663

    Article  Google Scholar 

  12. 12.

    Heikkala J (1995) Determining of cutting-force components in face milling. J Mater Process Technol 52(1):1–8

    Article  Google Scholar 

  13. 13.

    Guo D, Ren F, Sun Y (2010) An approach to modeling cutting forces in five-axis ball-end milling of curved geometries based on tool motion analysis. J Manuf Sci E Trans ASME, 132(4):575–590

  14. 14.

    Kilic ZM, Altintas Y (2016) Generalized mechanics and dynamics of metal cutting operations for unified simulations. Int J Mach Tool Manu 104:1–13

    Article  Google Scholar 

  15. 15.

    Wang JJJ, Zheng CM (2002) An analytical force model with shearing and ploughing mechanisms for end milling. Int J Mach Tool Manu 42(67):761–771

    Article  Google Scholar 

  16. 16.

    Campatelli G, Scippa A. (2012). Prediction of milling cutting force coefficients for Aluminum 6082-T4. In Wegener K (ed.), Procedia CIRP 1: 563–568

  17. 17.

    Song G, Li J, Sun J (2013) Approach for modeling accurate undeformed chip thickness in milling operation. Int J Adv Manuf Technol 68(5–8):1429–1439

    Article  Google Scholar 

  18. 18.

    Andersson C, Andersson M, Stahl JE (2011) Experimental studies of cutting force variation in face milling. Int J Mach Tool Manu 51(1):67–76

    Article  Google Scholar 

  19. 19.

    Zheng HQ, Li XP, Wong YS, Nee A (1999) Theoretical modelling and simulation of cutting forces in face milling with cutter runout. Int J Mach Tool Manu 39(12):2003–2018

    Article  Google Scholar 

  20. 20.

    Sun Y, Guo Q (2011) Numerical simulation and prediction of cutting forces in five-axis milling processes with cutter run-out. Int J Mach Tool Manu 51(10–11):806–815

    Article  Google Scholar 

  21. 21.

    Qu S, Zhao J, Wang T, Tian F (2015) Improved method to predict cutting force in end milling considering cutting process dynamics. Int J Adv Manuf Technol 78(9–12):1501–1510

    Article  Google Scholar 

  22. 22.

    Cai S, Yao B, Feng W, Cai Z (2019) An improved cutting force prediction model in the milling process with a multi-blade face milling cutter based on FEM and NURBS. Int J Adv Manuf Technol 104(5):2487–2499

    Article  Google Scholar 

  23. 23.

    Liu XW, Cheng K, Webb D, Longstaff AP, Widiyarto MH (2004) Improved dynamic cutting force model in peripheral milling. Part II: experimental verification and prediction. Int J Adv Manuf Technol 24(11–12):794–805

    Article  Google Scholar 

  24. 24.

    Pawełko P, Powałka B, Berczyński S (2013) Estimation of cutting force model coefficients with regularized inverse problem. Adv Manuf Sci Technol 37(2):5–21

    Google Scholar 

  25. 25.

    Rubeo MA, Schmitz TL (2016) Milling force modeling: a comparison of two approaches. Procedia Manuf 5:90–105

    Article  Google Scholar 

  26. 26.

    Lee P, Altintas Y (1996) Prediction of ball-end milling forces from orthogonal cutting data. Int J Mach Tool Manu 36(9):1059–1072

    Article  Google Scholar 

  27. 27.

    Shi Z, Liu L, Liu Z (2015) Influence of dynamic effects on surface roughness for face milling process. Int J Adv Manuf Technol 80(9–12):1823–1831

    Google Scholar 

  28. 28.

    Jin S, Liu S, Zhang X, Chen K (2019) A unified prediction model of 3D surface topography in face milling considering multi-error sources. Int J Adv Manuf Technol 102(1–4):705–717

    Article  Google Scholar 

  29. 29.

    Kilic ZM, Altintas Y (2016) Generalized modelling of cutting tool geometries for unified process simulation. Int J Mach Tool Manu 104:14–25

    Article  Google Scholar 

Download references

Funding

This work has been partially supported by the National Natural Science Foundation of China (Grant Nos. 51535007, 51675339, and 51975369). The authors would like to thank these financial supports.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sun Jin.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, S., Jin, S. Predicting milling force variation in time and space domain for multi-toothed face milling. Int J Adv Manuf Technol 108, 2269–2283 (2020). https://doi.org/10.1007/s00170-020-05319-5

Download citation

Keywords

  • Face milling
  • Milling force variation
  • Cutting coefficients
  • Response surface methodology
  • Experimental verification