Skip to main content
SpringerLink
Log in

A novel 3D surface topography prediction algorithm for complex ruled surface milling and partition process optimization

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The ruled surface is an important modeling method of the product. With high-performance requirement, more and more complex ruled surfaces with the characteristic of variant curvature are used in auto, aviation, or die mold. The time-varying tool position and orientation make the prediction of surface topography very difficult in the five-axis machine tool processing. In this paper, a three-dimensional surface topography prediction method is proposed. The point cloud of cutting edge is obtained after a series of matrix transformation by calculating the instant information of the local tool coordinate. Then, lots of tiny bounding boxes are constructed based on the profile of the workpiece. The 3D surface topography is obtained by the Boolean operation between the enveloping body of cutting edge and the tiny bounding boxes. The geometric error of the machine tool, the vibrations, and the deformation of the tool is also considered in the generation of the surface topography. The presented method is validated by a case study of the S test piece. The prediction results are verified by the measuring experiment at the same time. Finally, a partition optimization method on the surface topography for the complex surface of the S test piece is presented. The dynamic modification of the cutting parameters in different partitions of the milling process can greatly improve the surface quality without reducing efficiency.

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
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Xie D, Ding J, Liu F, Jiang Z, Du L, Wang W, Song Z (2014) Modeling errors forming abnormal tool marks on a twisted ruled surface in flank milling of the five-axis CNC. J Mech Sci Technol 28(11):4717–4726

    Article  Google Scholar 

  2. Lin RS, Koren Y (2000) Ruled surface machining on five-axis CNC machine tools. J Manuf Process 2(1):25–35

    Article  Google Scholar 

  3. Olvera D, Calleja A, Lacalle LNLD, Campa F, Lamikiz A (2012) Flank milling of complex surfaces - machining of complex sculptured surfaces. Springer, London, pp 1–31

    Book  Google Scholar 

  4. Gao T, Zhang W, Qiu K, Wan M (2006) Numerical simulation of machined surface topography and roughness in milling process. J Manuf Sci Eng 128(1):96–103

    Article  Google Scholar 

  5. Buj-Corral I, Vivancos-Calvet J (2011) Influence of feed, eccentricity and helix angle on topography obtained in side milling processes. Int J Mach Tools Manuf 51(12):889–897

    Article  Google Scholar 

  6. Wang Z, Wang B, Yuan J (2019) Modeling of surface topography based on cutting vibration in ball-end milling of thin-walled parts. Int J Adv Manuf Technol 101(5–8):1837–1854

    Article  Google Scholar 

  7. Li S, Dong Y, Li Y, Li P, Yang Z, Landers RG (2019) Geometrical simulation and analysis of ball-end milling surface topography. Int J Adv Manuf Technol 102(5–8):1885–1900

    Google Scholar 

  8. Arizmendi M, Fernández J, Gil A, Veiga F (2009) Effect of tool setting error on the topography of surfaces machined by peripheral milling. Int J Mach Tools Manuf 49(1):36–52

    Article  Google Scholar 

  9. Arizmendi M, Campa FJ, Fernández, de Lacalle LL, Gil A, Bilbao E, Lamikiz A (2012) Model for surface topography prediction in peripheral milling considering tool vibration. Cirp Ann-Manuf Tech 58(1):93–96

    Article  Google Scholar 

  10. Jiang H, Long X, Meng G (2008) Study of the correlation between surface generation and cutting vibrations in peripheral milling. J Mater Process Technol 208(1–3):229–238

    Article  Google Scholar 

  11. Costes JP, Moreau V (2011) Surface roughness prediction in milling based on tool displacements. J Manuf Process 13(2):133–140

    Article  Google Scholar 

  12. Wojciechowski S (2011) Machined surface roughness including cutter displacements in milling of hardened steel. Metrol Meas Syst 18(3):429–440

    Article  Google Scholar 

  13. Surmann T, Biermann D (2008) The effect of tool vibrations on the flank surface created by peripheral milling. CIRP Ann - Manuf Tech 57(1):375–378

    Article  Google Scholar 

  14. Arizmendi M, Campa FJ, Fernández J, de Lacalle LL, Gil A, Bilbao E, Lamikiz A (2009) Model for surface topography prediction in peripheral milling considering tool vibration. CIRP Ann - Manuf Tech 58(1):93–96

    Article  Google Scholar 

  15. 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(1–4):735–749

    Article  Google Scholar 

  16. Pimenov DY, Guzeev VI, Krolczyk G, Mia M, Wojciechowski S (2018) Modeling flatness deviation in face milling considering angular movement of the machine tool system components and tool flank wear. Precis Eng 54:327–337

    Article  Google Scholar 

  17. 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 91:43–53

    Article  Google Scholar 

  18. Denkena B, Köhler J, Sellmeier V, Mörke T (2011) Topography prediction of resilient parts after flank milling with chamfered tools. Prod Eng Res Devel 5(3):273–281

    Article  Google Scholar 

  19. 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(7–8):1263–1275

    Article  Google Scholar 

  20. Wojciechowski S, Matuszak M, Powałka B, Madajewski M, Maruda RW, Królczyk GM (2019) Prediction of cutting forces during micro end milling considering chip thickness accumulation. Int J Mach Tools Manuf 147:103466

    Article  Google Scholar 

  21. Li M, Huang J, Liu X, Wang J, Jia L (2018) Research on surface morphology of the ruled surface in five-axis flank milling. Int J Adv Manuf Technol 94(5–8):1–10

    Google Scholar 

  22. Hao W, Wan X J (2009) Prediction surface topography in flank milling. International Conference on Intelligent Robotics and Applications, Berlin, Heidelberg, December 2009, 5928:967–975

  23. Urbikain G, Lacalle LNLD (2018) Modelling of surface roughness in inclined milling operations with circle-segment end mills. Simul Model Pract Th 84:161–176

    Article  Google Scholar 

  24. Zhang X, Pan X, Wang G (2019) Influence factors of surface topography in micro-side milling. Int J Adv Manuf Technol 105(12):5239–5245

    Article  Google Scholar 

  25. Zhang X, Zhang J, Pang B, Zhao W (2016) An accurate prediction method of cutting forces in 5-axis flank milling of sculptured surface. Int J Mach Tools Manuf 104:26–36

    Article  Google Scholar 

  26. Yildiz AR (2013) A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Appl Soft Comput 13(3):1561–1566

    Article  Google Scholar 

  27. Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64(1–4):55–61

    Article  Google Scholar 

  28. Subramanian M, Sakthivel M, Sooryaprakash K, Sudhakaran R (2013) Optimization of cutting parameters for cutting force in shoulder milling of Al7075-T6 using response surface methodology and genetic algorithm. Procedia Eng 64(12):690–700

    Article  Google Scholar 

  29. Song Z, Wang L, Tao W, Wang W (2018) The mechanism of curvature for complex surfaces during five-axis flank milling. Int J Adv Manuf Technol 94:1665–1676

    Article  Google Scholar 

  30. Wang W, Jiang Z, Tao W, Zhuang W (2015) A new test part to identify performance of five-axis machine tool-part I. Int J Adv Manuf Technol 79:729–738

    Article  Google Scholar 

  31. Eberhart R, Kennedy J (1995) Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks 4:1942–1948

Download references

Funding

TThe authors would like to thank the Major Project of National Science and Technology (No. 2017ZX04002001), the Fundamental Research Funds for the Central Universities (ZYGX2019J032), NSAF (U1830110) and foundation of key laboratory of ultra-precision machining technology in Chinese academy of engineering physics (K1126-17-Y).

Author information

Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Wei Wang & Qingzhao Li

  2. Chengdu Aircraft Industrial (Group) Co., Ltd., Chengdu, 610092, China

    Yunfeng Jiang

Authors
  1. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingzhao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunfeng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Wang.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, W., Li, Q. & Jiang, Y. A novel 3D surface topography prediction algorithm for complex ruled surface milling and partition process optimization. Int J Adv Manuf Technol 107, 3817–3831 (2020). https://doi.org/10.1007/s00170-020-05263-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05263-4

Keywords

Search

Navigation