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The Visual Computer

, Volume 35, Issue 5, pp 639–651 | Cite as

Cost-effective printing of 3D objects with self-supporting property

  • Jidong Wang
  • Jiajia Dai
  • Kin-Sum Li
  • Jun Wang
  • Mingqiang Wei
  • Mingyong PangEmail author
Original Article

Abstract

The fused deposition modeling (FDM) printer is a simple, affordable and widely used device in the 3D printing society. However, the high price of printing materials is one of major restrictive factors for its further application. Based on the self-supporting property of printing materials, we present an optimization method to reduce the total material consumption of 3D printed objects themselves and their support structures for FDM printers in this paper. We first develop an orientation optimization scheme to reduce the outer support volume of a printed model. The volume is evaluated according to the depths of 3D model fragments obtained by the depth peeling technique in an optimization process. We then build a self-supporting frame with a set of scale-adaptive parallelepiped grids to replace the solid interior of the printed model for further reducing the material consumption. In our orientation optimization scheme, the overhanging area detecting function can detect the self-supporting regions of a 3D model in terms of the depths stored in the graphical processing unit memory. The self-supporting frame with grid structures inside printed models does not need to add additional support structures during the printing process. Experimental results indicate that our method is faster and consumes less printing materials than the state-of-the-art algorithms.

Keywords

3D printing Orientation optimization Self-supporting property Support structure Material consumption 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China [Grant No. 61502137], China Postdoctoral Science Foundation [Grant No. 2016M592047], the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. HKBU 12625716 and 12604017], the Chiang Ching-kuo Foundation for International Scholarly Exchange [No. RG025-P-15], the Faculty Research Grant Category II [HKBU No. FRG2/15-16/045] and Science Foundation of Chuzhou University [Grant No. 2014GH03].

References

  1. 1.
    Sá, A.M.E., Echavarria, K.R., Arnold, D.: Dual joints for 3D-structures. Vis. Comput. 30(12), 1321–1331 (2014)Google Scholar
  2. 2.
    Bermano, A.H., Funkhouser, T., Rusinkiewicz, S.: State of the art in methods and representations for fabrication aware design. Comput. Graph. Forum 36(2), 509–535 (2017)Google Scholar
  3. 3.
    Livesu, M., Ellero, S., Martínez, J., Lefebvre, S., Attene, M.: From 3D models to 3D prints: an overview of the processing pipeline. Comput. Graph. Forum 36(2), 537–564 (2017)Google Scholar
  4. 4.
    Wang, W., Wang, T.Y., Yang, Z., Liu, L., Tong, X., Tong, W., Deng, J., Chen, F., Liu, X.: Cost-effective printing of 3D objects with skin-frame structures. ACM Trans. Graph. 32(6), 2504–2507 (2013)Google Scholar
  5. 5.
    Lu, L., Sharf, A., Zhao, H., Wei, Y., Fan, Q., Chen, X., Savoye, Y., Tu, C., Cohen-Or, D., Chen, B.: Build-to-last: strength to weight 3D printed objects. ACM Trans. Graph. 33(4), 97 (2014)zbMATHGoogle Scholar
  6. 6.
    Zhang, X., Le, X., Panotopoulou, A., Whiting, E., Wang, C.C.L.: Perceptual models of preference in 3D printing direction. ACM Trans. Graph. 34(6), 1–12 (2015)Google Scholar
  7. 7.
    Everitt, C.: Interactive order-independent transparency. Technical report, NVidia (2001)Google Scholar
  8. 8.
    Lucidi, S., Sciandrone, M.: A derivative-free algorithm for bound constrained optimization. Comput. Optim. Appl. 22(2), 119–142 (2002)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Gao, W., Zhang, Y., Ramanujan, D., Ramani, K., Chen, Y., Williams, C.B., Wang, C.C.L., Shin, Y.C., Zhang, S., Zavattieri, P.D.: The status, challenges, and future of additive manufacturing in engineering. Comput. Aided Des. 69(C), 65–89 (2015)Google Scholar
  10. 10.
    Frank, D., Fadel, G.: Expert system-based selection of the preferred direction of build for rapid prototyping processes. J. Intell. Manuf. 6(5), 339–345 (1995)Google Scholar
  11. 11.
    Lan, P.T., Chou, S.Y., Chen, L.L., Gemmill, D.: Determining fabrication orientations for rapid prototyping with stereolithography apparatus. Comput. Aided Des. 29(1), 53–62 (1997)Google Scholar
  12. 12.
    Alexander, P., Allen, S., Dutta, D.: Part orientation and build cost determination in layered manufacturing. Comput. Aided Des. 30(5), 343–356 (1998)Google Scholar
  13. 13.
    Majhi, J., Janardan, R., Smid, M., Gupta, P.: On some geometric optimization problems in layered manufacturing. Comput. Geom. 12(3), 219–239 (1999)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Xu, F., Loh, H.T., Wong, Y.S.: Considerations and selection of optimal orientation for different rapid prototyping systems. Rapid Prototyp. J. 5(2), 54–60 (1999)Google Scholar
  15. 15.
    Yang, Y., Fuh, J.Y.H., Loh, H.T., Wong, Y.S.: Multi-orientational deposition to minimize support in the layered manufacturing process. J. Manuf. Syst. 22(2), 116–129 (2003)Google Scholar
  16. 16.
    Yang, Y., Fuh, J.Y.H., Loh, H.T., Wong, Y.S.: Multi-orientational deposition to minimize support in the layered manufacturing process. J. Manuf. Syst. 22(2), 116–129 (2003)Google Scholar
  17. 17.
    Phatak, A.M., Pande, S.S.: Optimum part orientation in rapid prototyping using genetic algorithm. J. Manuf. Syst. 31(4), 395–402 (2012)Google Scholar
  18. 18.
    Ezair, B., Massarwi, F., Elber, G.: Orientation analysis of 3D objects toward minimal support volume in 3D-printing. Comput. Graph. 51, 117–124 (2015)Google Scholar
  19. 19.
    Khardekar, R., McMains, S.: Fast layered manufacturing support volume computation on GPUs. In: ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 993–1002. American Society of Mechanical Engineers (2006)Google Scholar
  20. 20.
    Hu, K., Jin, S., Wang, C.C.: Support slimming for single material based additive manufacturing. Comput. Aided Des. 65(C), 1–10 (2015)Google Scholar
  21. 21.
    Huang, G., Huang, G., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)zbMATHGoogle Scholar
  22. 22.
    Zhang, Y., Bernard, A., Gupta, R.K., Harik, R.: Feature based building orientation optimization for additive manufacturing. Rapid Prototyp. J. 22(2), 358–376 (2016)Google Scholar
  23. 23.
    Morgan, H.D., Cherry, J.A., Jonnalaganna, S., Ewing, D., Sienz, J.: Part orientation optimisation for the additive layer manufacture of metal components. Int. J. Adv. Manuf. Technol. 29(1), 1–9 (2016)Google Scholar
  24. 24.
    Huang, X., Ye, C., Wu, S., Guo, K., Mo, J.: Sloping wall structure support generation for fused deposition modeling. Int. J. Adv. Manuf. Technol. 42(11–12), 1074–1081 (2009)Google Scholar
  25. 25.
    Strano, G., Hao, L., Everson, R.M., Evans, K.E.: A new approach to the design and optimisation of support structures in additive manufacturing. Int. J. Adv. Manuf. Technol. 66(9–12), 1247–1254 (2013)Google Scholar
  26. 26.
    Huang, P., Wang, C.C.L., Chen, Y.: Algorithms for Layered Manufacturing in Image Space. Advances in Computers and Information in Engineering Research, vol. 1. ASME, New York (2014)Google Scholar
  27. 27.
    Vanek, J., Galicia, J.A.G., Benes, B.: Clever support: efficient support structure generation for digital fabrication. Comput. Graph. Forum 33(5), 117–125 (2014)Google Scholar
  28. 28.
    Autodesk: Meshmixer. http://www.meshmixer.com/ (2017). Accessed 21 Mar 2017
  29. 29.
    Dumas, J., Hergel, J., Lefebvre, S.: Bridging the gap: automated steady scaffoldings for 3D printing. ACM Trans. Graph 33(4), 98 (2014)Google Scholar
  30. 30.
    Hornus, S., Lefebvre, S., Dumas, J., Claux, F.: Tight printable enclosures for additive manufacturing. J. Inst. Telev. Eng. Jpn. 28(12), 1017–1026 (2015)Google Scholar
  31. 31.
    Mirzendehdel, A.M., Suresh, K.: Support structure constrained topology optimization for additive manufacturing. Comput. Aided Des. 81, 1–13 (2016)Google Scholar
  32. 32.
    Stava, O., Vanek, J., Benes, B., Carr, N., Měch, R.: Stress relief: improving structural strength of 3D printable objects. ACM Trans. Graph. 31(4), 48 (2012)Google Scholar
  33. 33.
    Zhang, X., Xia, Y., Wang, J., Yang, Z., Tu, C., Wang, W.: Medial axis tree-an internal supporting structure for 3D printing. Comput. Aided. Geom. Des. 35, 149–162 (2015)MathSciNetzbMATHGoogle Scholar
  34. 34.
    Sá, A.M.E., Mello, V.M., Echavarria, K.R., Covill, D.: Adaptive voids. Vis. Comput. 31(6–8), 799–808 (2015)Google Scholar
  35. 35.
    Wu, J., Dick, C., Westermann, R.: A system for high-resolution topology optimization. IEEE Trans. Vis. Comput. Graph. 22(3), 1195–1208 (2016)Google Scholar
  36. 36.
    Li, D., Dai, N., Jiang, X., Chen, X.: Interior structural optimization based on the density-variable shape modeling of 3D printed objects. Int. J. Adv. Manuf. Technol. 83(9–12), 1–9 (2015)Google Scholar
  37. 37.
    Wu, J., Wang, C.C.L., Zhang, X., Westermann, R.: Self-supporting rhombic infill structures for additive manufacturing. Comput. Aided Des. 80, 32–42 (2016)Google Scholar
  38. 38.
    Xie, Y., Chen, X.: Support-free interior carving for 3D printing. Vis. Inf. 1(1), 9–15 (2017)Google Scholar
  39. 39.
    Lee, J., Lee, K.: Block-based inner support structure generation algorithm for 3D printing using fused deposition modeling. Int. J. Adv. Manuf. Technol. 89(5), 2151–2163 (2017)Google Scholar
  40. 40.
    Wang, W., Qian, S., Lin, L., Li, B., Yin, B., Liu, L., Liu, X.: Support-free frame structures. Comput. Graph. 66(Supplement C), 154–161 (2017)Google Scholar
  41. 41.
    Hornus, S., Lefebvre, S.: Iterative carving for self-supporting 3D printed cavities. [Research Report] RR-9083, Inria Nancy - Grand Est. 2017, pp.14Google Scholar
  42. 42.
    Guo, X., Zhou, J., Zhang, W., Du, Z., Liu, C., Liu, Y.: Self-supporting structure design in additive manufacturing through explicit topology optimization. Comput. Methods Appl. Mech. Eng. 323, 27–63 (2017)MathSciNetGoogle Scholar
  43. 43.
    Yang, Y., Chai, S., Fu, X.-M.: Computing interior support-free structure via hollow-to-fill construction. Comput. Graph. (2017).  https://doi.org/10.1016/j.cag.2017.07.005 Google Scholar
  44. 44.
    Lee, M., Fang, Q., Ryu, J., Liu, L., Kim, D.S.: Support-free hollowing for 3D printing via Voronoi diagram of ellipses. https://arxiv.org/abs/1708.06577 (2017). Accessed 21 Oct 2017
  45. 45.
    Wang, W., Liu, Y.J., Wu, J., Tian, S., Wang, C.C.L., Liu, L., Liu, X.: Support-free hollowing. IEEE Trans. Vis. Comput. Graph. (2017).  https://doi.org/10.1109/TVCG.2017.2764462 Google Scholar
  46. 46.
    Huybrechts, S., Tsai, S.W.: Analysis and behavior of grid structures. Compos. Sci. Technol. 56(9), 1001–1015 (1996)Google Scholar
  47. 47.
    Vasiliev, V.V., Barynin, V.A., Rasin, A.F.: Anisogrid lattice structures survey of development and application. Compos. struct. 54(2), 361–370 (2001)Google Scholar
  48. 48.
    Li, G., Cheng, J.: A generalized analytical modeling of grid stiffened composite structures. J. Compos. Mater. 41(24), 2939–2969 (2007)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Educational TechnologyNanjing Normal UniversityGulou DistrictChina
  2. 2.Department of HistoryHong Kong Baptist UniversityHong KongChina
  3. 3.College of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  4. 4.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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