Advertisement

Slicing point cloud incrementally for Additive Manufacturing via online learning

  • Tong Yang
  • Shan Yao
  • Kaihua XueEmail author
Original Article
  • 37 Downloads

Abstract

This paper reports an algorithm to chop point cloud into layer-wise slices for additive manufacturing. It starts with intersecting slicing plane with the 3D input points, generating planar samples. Then, an online learning model, known as competitive segments representation (CSR), extracts their implicit topology and distribution. CSR structure is a restricted graph that equals to multiple polylines, which are meanwhile piecewise linear approximation to the principal curves of samples. Edge segments of CSR compete with each other for representing consecutively given samples. They dynamically move, grow, shrink or rewire subject to several heuristic rules. Those rules are designed to depress abnormal data, enable lifelong learning, recover salient feature and ensure correct topology. Assembling them together allows online tracking of changing curves. Once CSR converges on one slice, learnt curves are reused as initial estimation for the next. By this practice, shape coherence of successive slices is efficiently utilized, and the ongoing learning output all subsequent slices incrementally. We have verified the feasibility of proposed algorithm both on synthesized data and scanned points.

Keywords

Additive Manufacturing Curve reconstruction Topology learning Principal curve 

Notes

Acknowledgements

This work was supported by the National High Technology Research and Development Program of China (Grant No. 2015AA042502). We are grateful to Xu Jinting for providing the PLSP implementation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Zhang L, Dong H, Saddik AE (2016) From 3d sensing to printing: a survey. ACM Trans Multimed Comput Commun Appl 12(2):27Google Scholar
  2. 2.
    Gao W, Zhang Y, Ramanujan D et al (2015) The status, challenges, and future of additive manufacturing in engineering. Comput Aided Des 69:65–89CrossRefGoogle Scholar
  3. 3.
    Mohan Pandey P, Venkata Reddy N, Dhande SG (2003) Slicing procedures in layered manufacturing: a review. Rapid Prototyp J 9(5):274–288CrossRefGoogle Scholar
  4. 4.
    Hastie T, Stuetzle W (1989) Principal curves. J Am Stat Assoc 84(406):502–516MathSciNetCrossRefGoogle Scholar
  5. 5.
    Fritzke B (1994) A growing neural gas network learns topologies. In: Proceedings of the 7th international conference on neural information processing systems. MIT Press, Cambridge, MA, USA, pp 625–632Google Scholar
  6. 6.
    Lee KH, Woo H (2000) Direct integration of reverse engineering and rapid prototyping. Comput Ind Eng 38(1):21–38MathSciNetCrossRefGoogle Scholar
  7. 7.
    Liu G, Wong Y, Zhang Y, Loh H (2003) Modelling cloud data for prototype manufacturing. J Mater Process Technol 138(1–3):53–57CrossRefGoogle Scholar
  8. 8.
    Wu Y, Wong Y, Loh H, Zhang Y (2004) Modelling cloud data using an adaptive slicing approach. Comput Aided Des 36(3):231–240CrossRefGoogle Scholar
  9. 9.
    Wang J, Yu Z, Zhang W et al (2014) Robust reconstruction of 2d curves from scattered noisy point data. Comput Aided Des 50(3):27–40CrossRefGoogle Scholar
  10. 10.
    Goes Fd, Cohen-Steiner D, Alliez P, Desbrun M (2011) An optimal transport approach to robust reconstruction and simplification of 2d shapes. Comput Graph Forum 30(5):1593–1602CrossRefGoogle Scholar
  11. 11.
    Chen JSS, Feng HY (2011) Contour generation for layered manufacturing with reduced part distortion. Int J Adv Manuf Technol 53(9–12):1103–1113CrossRefGoogle Scholar
  12. 12.
    Javidrad F, Pourmoayed AR (2011) Contour curve reconstruction from cloud data for rapid prototyping. Robot Comput Integr Manuf 27(2):397–404CrossRefGoogle Scholar
  13. 13.
    Xu J, Hou W, Sun Y, Lee YS (2018) Plsp based layered contour generation from point cloud for additive manufacturing. Robot Comput Integr Manuf 49:1–12CrossRefGoogle Scholar
  14. 14.
    Sun Y, Guo D, Jia Z, Liu W (2006) B-spline surface reconstruction and direct slicing from point clouds. Int J Adv Manuf Technol 27(9–10):918–924Google Scholar
  15. 15.
    Khameneifar F, Feng HY (2017) Extracting sectional contours from scanned point clouds via adaptive surface projection. Int J Prod Res 55(15):1–15CrossRefGoogle Scholar
  16. 16.
    Liu GH, Wong YS, Zhang YF, Loh HT (2003) Error-based segmentation of cloud data for direct rapid prototyping. Comput Aided Des 35(7):633–645CrossRefGoogle Scholar
  17. 17.
    Percoco G, Galantucci LM (2008) Local-genetic slicing of point clouds for rapid prototyping. Rapid Prototyp J 14(3):161–166CrossRefGoogle Scholar
  18. 18.
    Kumbhar VK, Pandey PM, Rao PVM (2008) Improved intermediate point curve model for integrating reverse engineering and rapid prototyping. Int J Adv Manuf Technol 37(5–6):553–562CrossRefGoogle Scholar
  19. 19.
    Yang P, Qian X (2007) Adaptive slicing of moving least squares surfaces: toward direct manufacturing of point set surfaces. J Manuf Sci Eng Trans ASME 8(3):433–442Google Scholar
  20. 20.
    Qiu Y, Zhou X, Qian X (2011) Direct slicing of cloud data with guaranteed topology for rapid prototyping. Int J Adv Manuf Technol 53(1–4):255–265CrossRefGoogle Scholar
  21. 21.
    Chen Y, Li K, Qian X (2013) Direct geometry processing for telefabrication. J Comput Inf Sci Eng 13(4):041002CrossRefGoogle Scholar
  22. 22.
    Yang P, Li K, Qian X (2011) Topologically enhanced slicing of mls surfaces. J Comput Inf Sci Eng 11(3):031003CrossRefGoogle Scholar
  23. 23.
    McMains S, Séquin C (1999) A coherent sweep plane slicer for layered manufacturing. In: Proceedings of the fifth ACM symposium on solid modeling and applications, pp 285–295Google Scholar
  24. 24.
    Minetto R, Volpato N, Stolfi J et al (2017) An optimal algorithm for 3d triangle mesh slicing. Comput Aided Des 92:1–10CrossRefGoogle Scholar
  25. 25.
    Yaman U, Butt N, Sacks E, Hoffmann C (2016) Slice coherence in a query-based architecture for 3d heterogeneous printing. Comput Aided Des 75(C):27–38CrossRefGoogle Scholar
  26. 26.
    Fritzke B (1997) A self-organizing network that can follow non-stationary distributions. In: Artificial neural networks—ICANN’97, pp 613–618Google Scholar
  27. 27.
    Araujo AFR, Rego RLME (2013) Self-organizing maps with a time-varying structure. ACM Comput Surv 46(1):1–38CrossRefGoogle Scholar
  28. 28.
    López-Rubio E (2010) Probabilistic self-organizing maps for continuous data. IEEE Trans Neural Netw 21(10):1543–1554CrossRefGoogle Scholar
  29. 29.
    Xing Y, Shi X, Shen F et al (2016) A self-organizing incremental neural network based on local distribution learning. Neural Netw 84:143–160CrossRefGoogle Scholar
  30. 30.
    Vaswani N, Bouwmans T, Javed S, Narayanamurthy P (2018) Robust subspace learning: robust PCA, robust subspace tracking and robust subspace recovery. IEEE Signal Process Mag 35(4):32–55CrossRefGoogle Scholar
  31. 31.
    López-Rubio E, Palomo EJ, Domínguez E (2015) Robust self-organization with m-estimators. Neurocomputing 151:408–423CrossRefGoogle Scholar
  32. 32.
    Angelopoulou A, Rodriguez JG, Orts-Escolano S et al (2018) Fast 2d/3d object representation with growing neural gas. Neural Comput Appl 29(10):903–919CrossRefGoogle Scholar
  33. 33.
    Löffler M, Kaiser M, van Kapel T et al (2014) The connect-the-dots family of puzzles: design and automatic generation. ACM Trans Graph 33(4):1–10CrossRefGoogle Scholar
  34. 34.
    Ohrhallinger S, Mitchell SA, Wimmer M (2016) Curve reconstruction with many fewer samples. Comput Graph Forum 35(5):167–176CrossRefGoogle Scholar
  35. 35.
    Fišer D, Faigl J, Kulich M (2013) Growing neural gas efficiently. Neurocomputing 104:72–82CrossRefGoogle Scholar
  36. 36.
    Orts-Escolano S, Garcia-Rodriguez J, Cazorla M et al (2018) Bioinspired point cloud representation: 3d object tracking. Neural Comput Appl 29:1–10CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Material Science and EngineeringDalian University of TechnologyDalianChina

Personalised recommendations