Advertisement

Study on tool orientation feasible region with constraint of non-linear error for high-precision five-axis machining

  • Jian-wei MaEmail author
  • Si-yu Chen
  • Guan-lin Li
  • Zi-wen Qu
  • Xiao Lu
ORIGINAL ARTICLE

Abstract

Five-axis machine is one of the most versatile machine tools in complex surface machining while the machining interference occurs easily. To avoid the adverse consequences caused by interference, interference detection, and feasible region, solution are proposed. However, with the required processing accuracy of the complex surface parts increasing, the common feasible region solution is generally difficult to achieve. The error caused by the tool orientation variation along the tool path, just the non-linear error, may exceed the specified tolerance. Considering that both the interference problem and the non-linear problem could be solved by modifying the tool orientation, this study proposes a feasible region solution for tool orientation with the constraint of non-linear error, which can avoid tool interference and achieve high geometric accuracy for tool path planning in the extreme high-precision machining. The detection region that corresponds to the tool orientation at cutter contact (CC) point is calculated to avoid the collision firstly, and the feasible region of tool orientation for a certain CC point is established. After building the tool path equations between two adjacent CC points, the relationship between the movement angles of rotation axis and the machining error is built. By considering the non-linear error of the different rotatory axes respectively, the calculation formula for the movement angle variation of the rotation axis is deduced. Finally, according to the single point non-interference feasible region of the tool angle, the non-interference feasible region for the extreme high-precision processing is constructed with the constraint of non-linear error. A comparative experiment is carried out to verify the proposed feasible region construction method. The experimental results show that with the proposed method, the profile arithmetic average error and the maximum of profile deviation are 53 μm and 71 μm, respectively, which decrease by 30.13% and 32.31% compared with the conventional non-interference feasible region. The achievements have good applicability and are significant for the improvement of processing quality, which provide guidance for the extreme high-precision machining of complex surface parts.

Keywords

Feasible region Interference detection Non-linear error High-precision machining Five-axis machining 

Notes

Acknowledgements

The authors wish to thank the anonymous reviewers for their comments which led to improvements of this paper.

Funding information

The project is supported by National Key Research and Development Program of China (No. 2018YFA0703304), Science Challenge Project of China (No. TZ2018006-0101-02), National Natural Science Foundation of China (No. 51675081 and No. 51575087), Science and Technology Innovation Fund of Dalian (No. 2018J12GX038), Innovation Project for Supporting High-level Talent in Dalian (No. 2016RQ012), Science Fund for Creative Research Groups (No. 51621064), and the Fundamental Research Funds for the Central Universities.

References

  1. 1.
    Liang H, Hong H, Svoboda J (2002) A combined 3D linear and circular interpolation technique for multi-axis CNC machining. J Manuf Sci Eng 124(2):305–312CrossRefGoogle Scholar
  2. 2.
    Jia ZY, Zhao XX, Ma JW, Chen SY, Qin FZ, Liu Z (2019) Toolpath generation in sub-regional processing with constraint of constant scallop-height at boundary for complex curved surface. Precis Eng 55:217–230CrossRefGoogle Scholar
  3. 3.
    Kono D, Matsubara A, Yamaji I, Fujita T (2008) High-precision machining by measurement and compensation of motion error. Int J Mach Tools Manuf 48:1103–1110CrossRefGoogle Scholar
  4. 4.
    Gebhardt M, Mayr J, Furrer N, Widmer T, Weikert S, Knapp W (2014) High precision grey-box model for compensation of thermal errors on five-axis machines. CIRP Ann Manuf Technol 63(1):509–512CrossRefGoogle Scholar
  5. 5.
    Jiang Z, Ding JX, Zhang J, Du L, Wang W (2018) Research on error tracing method of five-axis CNC machine tool linkage error. J Braz Soc Mech Sci Eng 40(4):1–12CrossRefGoogle Scholar
  6. 6.
    Zhang LQ, Yue M (2011) Collision-free tool path generation for five-axis high speed machining. Key Eng Mater 474:961–966CrossRefGoogle Scholar
  7. 7.
    Luo SM, Liao LX, Wang J, Wang Y, Yi JX (2017) Study on inspection and avoidance of interferences in five-axis end milling of cycloidal gears. Int J Adv Manuf Technol 91(9):3307–3314CrossRefGoogle Scholar
  8. 8.
    Wang GX, Shu QL, Wang J, Wang WS (2014) Tool interference checking for five axis NC machining of sculptured surfaces. China Mech Eng 25(03):299–303Google Scholar
  9. 9.
    Lee RS, Lee JN (2001) Interference-free tool orientation determination by a virtual enveloping element for five-axis machining of a freeform surface. Proc Inst Mech Eng B J Eng Manuf 215(12):1683–1693CrossRefGoogle Scholar
  10. 10.
    Chen L, Xu K, Tang K (2015) Collision-free tool orientation optimization in five-axis machining of bladed disk. J Comput Des Eng 2(4):197–205Google Scholar
  11. 11.
    Tang TD (2014) Algorithms for collision detection and avoidance for five-axis NC machining: a state of the art review. Comput Aided Des 51:1–17CrossRefGoogle Scholar
  12. 12.
    Chen T, Ye PQ, Wang JS (2005) Local interference detection and avoidance in five-axis NC machining of sculptured surfaces. Int J Adv Manuf Technol 25(3–4):343–349CrossRefGoogle Scholar
  13. 13.
    Lee YS, Chang TC (1995) 2-phase approach to global interference avoidance in five-axis machining. Comput Aided Des 27(10):715–729CrossRefGoogle Scholar
  14. 14.
    Ding S, Mannan MA, Poo AN (2004) Oriented bounding box and octree based global interference detection in five-axis machining of free-form surfaces. Comput Aided Des 36(13):1281–1294CrossRefGoogle Scholar
  15. 15.
    Gray P, Bedi S, Ismail F (2003) Rolling ball method for five-axis surface machining. Comput Aided Des 35(4):347–357CrossRefGoogle Scholar
  16. 16.
    Gray P, Ismail F, Bedi S (2004) Graphics-assisted rolling ball method for five-axis surface machining. Comput Aided Des 36(7):653–663CrossRefGoogle Scholar
  17. 17.
    Gray P, Bedi S, Ismail F (2005) Arc-intersect method for five-axis tool positioning. Comput Aided Des 37(7):663–674CrossRefGoogle Scholar
  18. 18.
    Woo TC (1994) Visibility maps and spherical algorithms. Comput Aided Des 26(1):6–16CrossRefGoogle Scholar
  19. 19.
    Keeler T, Fedorkiw J, Ghali S (2007) The spherical visibility map. Comput Aided Des 39(1):17–26CrossRefGoogle Scholar
  20. 20.
    Liu M, Liu YS, Ramani K (2009) Computing global visibility maps for regions on the boundaries of polyhedra using Minkowski sums. Comput Aided Des 41(9):668–680CrossRefGoogle Scholar
  21. 21.
    Wang QH, Li JR, Zhou RR (2006) Graphics-assisted approach to rapid collision detection for multi-axis machining. Int J Adv Manuf Technol 30(9):853–863CrossRefGoogle Scholar
  22. 22.
    Liu HJ, Zhang AG, Zhao JB, Shang J, Liu J (2014) Analysis and compensation strategy of non-linear error in five-axis CNC machining. Appl Mech Mater 644:4967–4970CrossRefGoogle Scholar
  23. 23.
    She CH, Chang CC (2007) Design of a generic five-axis postprocessor based on generalized kinematics model of machine tool. Int J Mach Tools Manuf 47(3):537–545CrossRefGoogle Scholar
  24. 24.
    Zhang K, Zhang LQ, Yan YC (2016) Single spherical angle linear interpolation for the control of non-linearity errors in five-axis flank milling. Int J Adv Manuf Technol 87(9):3289–3299CrossRefGoogle Scholar
  25. 25.
    Tutunea-Fatan OR, Bhuiya MSH (2011) Comparing the kinematic efficiency of five-axis machine tool configurations through nonlinearity errors. Comput Aided Des 43(9):1163–1172CrossRefGoogle Scholar
  26. 26.
    Liang H, Hong H, Svoboda J (2003) A cutter orientation modification method for the reduction of non-linearity errors in five-axis CNC machining. Mach Sci Technol 7(1):1–18CrossRefGoogle Scholar
  27. 27.
    Zhang Y (2012) A kind of five-axis dynamic precision measure tool based on the RTCP function. Manuf Technol Mach Tool 11:92–94Google Scholar
  28. 28.
    Zhang YN, Liu K, Zhao DB, Lu YH (2012) Determination of collision-free tool orientation for RTCP. China Mech Eng 23(09):1009–1013Google Scholar
  29. 29.
    Zou X, Tam HY, Xu HY, Shi K (2019) Flat-end tool orientation based on rotation-minimizing frame. Adv Manuf 7:257–269CrossRefGoogle Scholar
  30. 30.
    Cui XS, Wei JF, Wei HG, Yang C (2013) Research on control strategy of nonlinear errors and influencing factors in five-axis NC machining. Manuf Technol Mach Tool 11:122–126Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  • Jian-wei Ma
    • 1
    Email author
  • Si-yu Chen
    • 1
  • Guan-lin Li
    • 1
  • Zi-wen Qu
    • 1
  • Xiao Lu
    • 1
  1. 1.Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, School of Mechanical EngineeringDalian University of TechnologyDalianChina

Personalised recommendations