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Fast update of sliced voxel workpiece models using partitioned swept volumes of three-axis linear tool paths

  • Jimin Joy
  • Hsi-Yung FengEmail author
Original Article
  • 20 Downloads

Abstract

This paper presents a new and effective method to achieve fast machining simulation via the frame-sliced voxel representation (FSV-rep) workpiece modeling environment. The FSV-rep workpiece modeling scheme has been demonstrated as an efficient and accurate geometric modeling format for simulating general milling operations. The method presented in this paper specifically targets linear tool paths in three-axis milling. Based on the boundary representation of the linear tool swept volume and the voxel space for modeling the workpiece, it partitions the swept volume into a set of elemental regions, named as the swept volume regions (SVRs). With SVRs, the best performance of FSV-rep workpiece model update in three-axis milling is achieved. In the representative case studies, up to an order of magnitude faster computational performance has been observed compared to the generic approach of approximating the tool swept volume as a set of sampled tool instances. In addition, the quality of the simulated machined surface is much improved due to the use of non-approximated tool swept volumes. The efficient way to use SVRs in FSV-rep workpiece model update and the specific qualities of SVRs enabling the observed superior performance are also discussed in the paper.

Keywords

Machining simulation Computational efficiency Three-axis milling Linear tool path Tool swept volume 

Notes

Acknowledgements

This research has been funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) under the CANRIMT Strategic Network Grant as well as the Discovery Grant.

References

  1. 1.
    Kersting P, Zabel A (2009) Optimizing NC-tool paths for simultaneous five-axis milling based on multi-population multi-objective evolutionary algorithms. Adv Eng Softw 40(6):452–463zbMATHGoogle Scholar
  2. 2.
    Manav C, Bank HS, Lazoglu I (2013) Intelligent toolpath selection via multi-criteria optimization in complex sculptured surface milling. J Intell Manuf 24(2):349–355Google Scholar
  3. 3.
    Fountas NA, Vaxevanidis NM, Stergiou CI, Benhadj-Djilali R (2017) A virus-evolutionary multi-objective intelligent tool path optimization methodology for 5-axis sculptured surface CNC machining. Eng Comput 33(3):375–391Google Scholar
  4. 4.
    Li DY, Peng YH, Yin ZW (2006) Interference detection for direct tool path generation from measured data points. Eng Comput 22(1):25–31Google Scholar
  5. 5.
    Chen ZC, Fu Q (2014) An efficient, accurate approach to medial axis transforms of pockets with closed free-form boundaries. Eng Comput 30(1):111–123MathSciNetGoogle Scholar
  6. 6.
    Fountas NA, Benhadj-Djilali R, Stergiou CI, Vaxevanidis NM (2017) An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence. J Intell Manuf 30(4):1581–1599Google Scholar
  7. 7.
    Fountas NA, Vaxevanidis NM, Stergiou CI, Benhadj-Djilali R (2018) Globally optimal tool paths for sculptured surfaces with emphasis to machining error and cutting posture smoothness. Int J Prod Res.  https://doi.org/10.1080/00207543.2018.1530468 Google Scholar
  8. 8.
    Joy J, Feng HY (2017) Frame-sliced voxel representation: an accurate and memory-efficient modeling method for workpiece geometry in machining simulation. Comput Aided Des 88:1–13Google Scholar
  9. 9.
    Joy J, Feng HY (2017) Efficient milling part geometry computation via three-step update of frame-sliced voxel representation workpiece model. Int J Adv Manuf Technol 92(5–8):2365–2378Google Scholar
  10. 10.
    Fleisig RV, Spence AD (2001) A constant feed and reduced angular acceleration interpolation algorithm for multi-axis machining. Comput Aided Des 33(1):1–15Google Scholar
  11. 11.
    Langeron JM, Duc E, Lartigue C, Bourdet P (2004) A new format for 5-axis tool path computation, using Bspline curves. Comput Aided Des 36(12):1219–1229Google Scholar
  12. 12.
    Sullivan A, Erdim H, Perry RN, Frisken SF (2012) High accuracy NC milling simulation using composite adaptively sampled distance fields. Comput Aided Des 44(6):522–536Google Scholar
  13. 13.
    Sarkar S, Dey PP (2015) Tolerance constraint CNC tool path modeling for discretely parameterized trimmed surfaces. Eng Comput 31(4):763–773Google Scholar
  14. 14.
    Aras E, Feng HY (2011) Vector model-based workpiece update in multi-axis milling by moving surface of revolution. Int J Adv Manuf Technol 52(9–12):913–927Google Scholar
  15. 15.
    Lee SW, Nestler A (2011) Complete swept volume generation, Part I: swept volume of a piecewise C1-continuous cutter at five-axis milling via Gauss map. Comput Aided Des 43(4):427–441Google Scholar
  16. 16.
    Lee SW, Nestler A (2011) Complete swept volume generation—part II: NC simulation of self-penetration via comprehensive analysis of envelope profiles. Comput Aided Des 43(4):442–456Google Scholar
  17. 17.
    Weinert K, Du S, Damm P, Stautner M (2004) Swept volume generation for the simulation of machining processes. Int J Mach Tools Manuf 44(6):617–628Google Scholar
  18. 18.
    Yang Y, Zhang W, Wan M, Ma Y (2013) A solid trimming method to extract cutter-workpiece engagement maps for multi-axis milling. Int J Adv Manuf Technol 68(9):2801–2813Google Scholar
  19. 19.
    Gong X, Feng HY (2016) Cutter-workpiece engagement determination for general milling using triangle mesh modeling. J Comput Des Eng 3(2):151–160Google Scholar
  20. 20.
    Ferry W, Yip-Hoi D (2008) Cutter-workpiece engagement calculations by parallel slicing for five-axis flank milling of jet engine impellers. ASME J Manuf Sci Eng 130(5):051011Google Scholar
  21. 21.
    Chung YC, Park JW, Shin H, Choi BK (1998) Modeling the surface swept by a generalized cutter for NC verification. Comput Aided Des 30(8):587–594zbMATHGoogle Scholar
  22. 22.
    Altintas Y, Engin S (2001) Generalized modeling of mechanics and dynamics of milling cutters. CIRP Ann Manuf Technol 50(1):25–30Google Scholar
  23. 23.
    Shmakov SL (2011) A universal method of solving quartic equations. Int J Pure Appl Math 71(2):251–259MathSciNetzbMATHGoogle Scholar
  24. 24.
    Fortune S (1987) A sweepline algorithm for Voronoi diagrams. Algorithmica 2:153–174MathSciNetzbMATHGoogle Scholar
  25. 25.
    Lee SW, Nestler A (2012) Virtual workpiece: workpiece representation for material removal process. Int J Adv Manuf Technol 58(5–8):443–463Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringThe University of British ColumbiaVancouverCanada

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