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A stress-based topology optimization method by a Voronoi tessellation Additive Manufacturing oriented

  • Filippo CucinottaEmail author
  • Marcello Raffaele
  • Fabio Salmeri
ORIGINAL ARTICLE
  • 13 Downloads

Abstract

The work presents a stress-based algorithm developed for the topology optimization of 3D surfaces. The novelty of the proposed methodology consists in the fact that it acts directly on a CAD level, and not on the mesh as is more usual. This allows to obtain a CAD ready to be manufactured with Additive Manufacturing technologies, without any subsequent intervention by the designer. The CAD algorithm is written in Rhino-Grasshopper environment and it is suitable to any FEM software. The methodology consists in a hollowing of the surface, starting by a Voronoi tessellation, allowing the designer to set a lot of parameters, as the number of control points, the dimension of the holes and the thickness of the branches of the tessellation. An iterative process leads to redraw at each iteration the Voronoi scheme in order to add material where the stress is higher and to remove it where the stress is lower. As a case study, in order to show the characteristics of the methodology, a seat for powerboats applications has been tested and optimized. The results from the case study demonstrate the high performance of the method and the capability to obtain in easy way light weight structures oriented for the Additive Manufacturing new technologies.

Keywords

Topology optimization Additive Manufacturing Selective laser sintering Mechanical design 

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Notes

Acknowledgments

The authors wish to thank the Union Internationale Motonautique (UIM) for providing materials and information and Tom Stanley, Sergio Abrami, and Sebastiano Pellecchia for the precious comments and suggestions.

References

  1. 1.
    Mortazavi A, Togan V (2016) Simultaneous size, shape, and topology optimization of truss structures using integrated particle swarm optimizer. Struct. Multidiscip. Optim. 54:715–736.  https://doi.org/10.1007/s00158-016-1449-7 MathSciNetCrossRefGoogle Scholar
  2. 2.
    Müller TE, van der Klashorst E (2017) A quantitative comparison between size, shape, topology and simultaneous optimization for truss structures. Lat. Am. J. Solids Struct. 14:2221–2242.  https://doi.org/10.1590/1679-78253900 CrossRefGoogle Scholar
  3. 3.
    G. Savio, R. Meneghello, G. Concheri, Optimization of lattice structures for Additive Manufacturing Technologies, in: B. Eynard, V. Nigrelli, S.M. Oliveri, G. Peris-Fajarnes, S. Rizzuti (Eds.), Adv. Mech. Des. Eng. Manuf. Proc. Int. Jt. Conf. Mech. Des. Eng. Adv. Manuf. (JCM 2016), 14-16 Sept. 2016, Catania, Italy, Springer International Publishing, Cham, 2017: pp. 213–222. doi: https://doi.org/10.1007/978-3-319-45781-9_22.
  4. 4.
    Panesar A, Abdi M, Hickman D, Ashcroft I (2018) Strategies for functionally graded lattice structures derived using topology optimisation for additive manufacturing. Addit. Manuf. 19:81–94.  https://doi.org/10.1016/j.addma.2017.11.008 CrossRefGoogle Scholar
  5. 5.
    Dehghanghadikolaei A, Namdari N, Mohammadian B., (2018), Additive manufacturing methods: a brief overview, J. Sci. Eng. Res. .Google Scholar
  6. 6.
    Gardan J (2016) Additive manufacturing technologies: state of the art and trends. Int. J. Prod. Res. 54:3118–3132.  https://doi.org/10.1080/00207543.2015.1115909 CrossRefGoogle Scholar
  7. 7.
    Chong L, Ramakrishna S, Singh S (2018) A review of digital manufacturing-based hybrid additive manufacturing processes. Int. J. Adv. Manuf. Technol. 95:2281–2300.  https://doi.org/10.1007/s00170-017-1345-3 CrossRefGoogle Scholar
  8. 8.
    Saboori A, Gallo D, Biamino S, Fino P, Lombardi M (2017) An overview of additive manufacturing of titanium components by directed energy deposition: microstructure and mechanical properties. Appl. Sci. 7:883.  https://doi.org/10.3390/app7090883 CrossRefGoogle Scholar
  9. 9.
    Graziosi S, Rosa F, Casati R, Solarino P, Vedani M, Bordegoni M (2017) Designing for metal additive manufacturing: a case study in the professional sports equipment field. Procedia Manuf. 11:1544–1551.  https://doi.org/10.1016/j.promfg.2017.07.288 CrossRefGoogle Scholar
  10. 10.
    Cucinotta F, Guglielmino E, Longo G, Risitano G, Santonocito D, Sfravara F (2019) Topology optimization additive manufacturing-oriented for a biomedical application. Adv. Mech. Des. Eng. Manuf. II.  https://doi.org/10.1007/978-3-030-12346-8_18
  11. 11.
    Singh S, Ramakrishna S (2017) Biomedical applications of additive manufacturing: present and future. Curr. Opin. Biomed. Eng. 2:105–115.  https://doi.org/10.1016/j.cobme.2017.05.006 CrossRefGoogle Scholar
  12. 12.
    Cucinotta F, Guglielmino E, Risitano G, Sfravara F (2016) Assessment of damage evolution in sandwich composite material subjected to repeated impacts by means optical measurements. Procedia Struct. Integr. 2:3660–3667.  https://doi.org/10.1016/j.prostr.2016.06.455 CrossRefGoogle Scholar
  13. 13.
    Cucinotta F, Paoli A, Risitano G, Sfravara F (2017) Optical measurements and experimental investigations in repeated low-energy impacts in powerboat sandwich composites. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 232:234–244.  https://doi.org/10.1177/1475090217720619 Google Scholar
  14. 14.
    Wang X, Ying X, Liu YJ, Xin SQ, Wang W, Gu X, Mueller-Wittig W, He Y (2015) Intrinsic computation of centroidal Voronoi tessellation (CVT) on meshes. CAD Comput. Aided Des. 58:51–61.  https://doi.org/10.1016/j.cad.2014.08.023 CrossRefGoogle Scholar
  15. 15.
    Fotovvati B, Wayne SF, Lewis G, Asadi E (2018) A review on melt-pool characteristics in laser welding of metals. Adv. Mater. Sci. Eng. 2018:1–18.  https://doi.org/10.1155/2018/4920718 CrossRefGoogle Scholar
  16. 16.
    Murr LE, Gaytan SM, Ceylan A, Martinez E, Martinez JL, Hernandez DH, Machado BI, Ramirez DA, Medina F, Collins S, Wicker RB (2010) Characterization of titanium aluminide alloy components fabricated by additive manufacturing using electron beam melting. Acta Mater. 58:1887–1894.  https://doi.org/10.1016/j.actamat.2009.11.032 CrossRefGoogle Scholar
  17. 17.
    Fotovvati B, Namdari N, Dehghanghadikolaei A (2019) Fatigue performance of selective laser melted Ti6Al4V components: state of the art. Mater. Res. Express. 6:0–14.  https://doi.org/10.1088/2053-1591/aae10e Google Scholar
  18. 18.
    (2011) Material data sheet EOS titanium Ti64 material data sheet technical data, 49 1–5. doi: https://doi.org/10.1099/mic.0.051441-0, Linear plasmids mobilize linear but not circular chromosomes in Streptomyces: support for the 'end first' model of conjugal transfer.
  19. 19.
  20. 20.
    Chen F, Brown GM, Song M (2000) Overview of 3-D shape measurement using optical methods. Opt. Eng. 39:13–39.  https://doi.org/10.1117/1.602438 Google Scholar
  21. 21.
    Barone S, Paoli A, Razionale AV (2012) Three-dimensional point cloud alignment detecting fiducial markers by structured light stereo imaging. Mach. Vis. Appl. 23:217–229.  https://doi.org/10.1007/s00138-011-0340-1 CrossRefGoogle Scholar
  22. 22.
    Niinomi M, Kuo CK, Ma PX (1998) Niinomi-1998-Mechanical propertie.pdf. Biomaterials 22:511–521.  https://doi.org/10.1016/S0142-9612(00)00201-5. Google Scholar
  23. 23.
    T. Dobbins, I. Rowley, L. Campbell (2008), High speed craft human factors engineering design guide, 120. doi:10.1093/mp/ssq038.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of EngineeringUniversity of MessinaMessinaItaly

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