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A unified prediction model of 3D surface topography in face milling considering multi-error sources

  • Sun Jin
  • Shun LiuEmail author
  • Xueping Zhang
  • Kun Chen
ORIGINAL ARTICLE
  • 26 Downloads

Abstract

Surface topography, which represents machined surface errors in 3D space, is a comprehensive way to estimate surface quality in face milling. This paper presents a unified simulation model for the prediction of 3D machined surface topography considering multiple error sources in face milling with multi-tooth cutter that has large diameter. In this model, the final machined surface topography is described with a height-encoded and position-maintained colorful surface image that consists of multi-scale errors. It is derived from a point cloud of residual surface height at each encoding contact location. The model includes the effects of milling parameters, different kinds of initial setup errors, and process static/dynamic characteristics of machine tool-workpiece-fixture system. The influences of inserts’ geometric structures on roughness scale are also introduced through beam elements. A numerical algorithm is proposed to obtain the resultant point cloud based on the simulation model integrating with all the effects of influence factors. Face milling experiments are conducted to validate and investigate the proposed simulation model. Comparisons between simulated and experimental results show good agreement. And the proposed model can also be applied to investigate the topography patterns induced by multiple error sources coupled and respectively. Thus provides a comprehension methodology to predict machined surface topography and the resultant topography patterns induced by multi-error sources in face milling for its industry implementation.

Keywords

3D machined surface topography Surface roughness Face milling Integrated error sources Multi-tooth cutter Validation 

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Notes

Funding information

This research is supported by the National Natural Science Foundation of China under project No. 51535007, the National Natural Science Foundation of China under project No. 51675339, and Shanghai GM PTME Company under project No. PS23005233.

References

  1. 1.
    Nguyen HT, Wang H, Tai BL, Ren J, Jack Hu S, Shih A (2016) High-definition metrology enabled surface variation control by cutting load balancing. J Manuf Sci Eng-Trans ASME 138:21010CrossRefGoogle Scholar
  2. 2.
    Wang M, Shao YP, Du SC, Xi LF (2015) A diffusion filter for discontinuous surface measured by high definition metrology. Int J Precis Eng Manuf 16:2057–2062CrossRefGoogle Scholar
  3. 3.
    Franco P, Estrems M, Faura F (2008) A study of back cutting surface finish from tool errors and machine tool deviations during face milling. Int J Mach Tools Manuf 48:112–123CrossRefGoogle Scholar
  4. 4.
    Zhang C, Zhang H, Li Y, Zhou L (2015) Modeling and on-line simulation of surface topography considering tool wear in multi-axis milling process. Int J Adv Manuf Technol 77:735–749CrossRefGoogle Scholar
  5. 5.
    Ghosh G, Mandal P, Mondal SC (2017) Modeling and optimization of surface roughness in keyway milling using ANN, genetic algorithm, and particle swarm optimization. Int J Adv Manuf Technol 2:1–20Google Scholar
  6. 6.
    Liu S, Jin S, Zhang X, Chen K, Wang L, Zhao H (2018) Optimization of 3D surface roughness induced by milling operation for adhesive-sealing. Procedia CIRP 71:279–284CrossRefGoogle Scholar
  7. 7.
    Suriano S, Wang H, Shao C, Hu SJ, Sekhar P (2015) Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations. IIE Trans 47:1033–1052CrossRefGoogle Scholar
  8. 8.
    Wang M, Xi L, Du S (2014) 3D surface form error evaluation using high definition metrology. Precis Eng 38:230–236CrossRefGoogle Scholar
  9. 9.
    Deltombe R, Bigerelle M, Jourani A (2015) Analysis of the effects of different machining processes on sealing using multiscale topography. Surf Topogr-Metrol Prop 4:15003CrossRefGoogle Scholar
  10. 10.
    Liu S, Jin S, Zhang XP, Wang LX, Mei BF, Hu B (2017) Controlling topography of machined surface for adhesive-sealing. Proceedings of the ASME 2017 12th International Manufacturing Science and Engineering Conference, Los Angeles, CA, USAGoogle Scholar
  11. 11.
    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 Eng-Trans ASME 135:41014CrossRefGoogle Scholar
  12. 12.
    Shi Z, Liu L, Liu Z (2015) Influence of dynamic effects on surface roughness for face milling process. Int J Adv Manuf Technol 80:1823–1831CrossRefGoogle Scholar
  13. 13.
    Nguyen HT, Wang H, Hu SJ (2014) Modeling cutter tilt and cutter-spindle stiffness for machine condition monitoring in face milling using high-definition surface metrology. Int J Adv Manuf Technol 70:1323–1335CrossRefGoogle Scholar
  14. 14.
    Gu F, Melkote SN, Kapoor SG, Devor RE (1997) A model for the prediction of surface flatness in face milling. J Manuf Sci Eng-Trans ASME 119:476–484CrossRefGoogle Scholar
  15. 15.
    Yang F, Jin S, Li Z (2016) A comprehensive study of linear variation propagation modeling methods for multistage machining processes. Int J Adv Manuf Technol 90:2139–2151CrossRefGoogle Scholar
  16. 16.
    Yang F, Jin S, Li Z, Ding S, Ma X (2017) A new error compensation model for machining process based on differential motion vectors. Int J Adv Manuf Technol 93:2943–2954CrossRefGoogle Scholar
  17. 17.
    Paris H, Peigne G, Mayer R (2004) Surface shape prediction in high speed milling. Int J Mach Tools Manuf 44:1567–1576CrossRefGoogle Scholar
  18. 18.
    Paris H, Peigné G, Brissaud D (2003) A model of milled surface generation for time domain simulation of high-speed cutting. Proc Inst Mech Eng Part B-J Eng Manuf 217:919–930CrossRefGoogle Scholar
  19. 19.
    Lavernhe S, Tournier C, Lartigue C (2008) Kinematical performance prediction in multi-axis machining for process planning optimization. Int J Adv Manuf Technol 37:534–544CrossRefGoogle Scholar
  20. 20.
    Honeycutt A, Schmitz TL (2017) A study of milling surface quality during period-2 bifurcations. Procedia Manuf 10:183–193CrossRefGoogle Scholar
  21. 21.
    Dépincé P, Hascoët J (2006) Active integration of tool deflection effects in end milling. Part 1. Prediction of milled surfaces. Int J Mach Tools Manuf 46:937–944CrossRefGoogle Scholar
  22. 22.
    Pimenov DY (2013) Geometric model of height of microroughness on machined surface taking into account wear of face mill teeth. J Frict Wear 34:290–293CrossRefGoogle Scholar
  23. 23.
    Lavernhe S, Quinsat Y, Lartigue C (2010) Model for the prediction of 3D surface topography in 5-axis milling. Int J Adv Manuf Technol 51:915–924CrossRefGoogle Scholar
  24. 24.
    Quinsat Y, Lavernhe S, Lartigue C (2011) Characterization of 3D surface topography in 5-axis milling. Wear 271:590–595CrossRefGoogle Scholar
  25. 25.
    Zhang W, Tan G, Wan M, Gao T, Bassir DH (2008) A new algorithm for the numerical simulation of machined surface topography in multiaxis ball-end milling. J Manuf Sci Eng-Trans ASME 130:11003CrossRefGoogle Scholar
  26. 26.
    Omar OEEK, El-Wardany T, Ng E, Elbestawi MA (2007) An improved cutting force and surface topography prediction model in end milling. Int J Mach Tools Manuf 47:1263–1275CrossRefGoogle Scholar
  27. 27.
    Song G, Li J, Sun J (2013) Approach for modeling accurate undeformed chip thickness in milling operation. Int J Adv Manuf Technol 68:1429–1439CrossRefGoogle Scholar
  28. 28.
    Yang D, Liu Z (2015) Surface plastic deformation and surface topography prediction in peripheral milling with variable pitch end mill. Int J Mach Tools Manuf 91:43–53CrossRefGoogle Scholar
  29. 29.
    Dong Z, Jiao L, Wang X, Liang Z, Liu Z, Yi J (2016) FEA-based prediction of machined surface errors for dynamic fixture-workpiece system during milling process. Int J Adv Manuf Technol 85:299–315CrossRefGoogle Scholar
  30. 30.
    Felhő C, Karpuschewski B, Kundrák J (2015) Surface roughness modelling in face milling. Procedia CIRP 31:136–141CrossRefGoogle Scholar
  31. 31.
    Lu Y, Ding Y, Zhu L (2017) Dynamics and stability prediction of five-axis flat-end milling. J Manuf Sci Eng-Trans ASME 139:61015CrossRefGoogle Scholar
  32. 32.
    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 78:1501–1510CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Mechanical System and Vibration, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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