Advertisement

Acta Mechanica

, Volume 229, Issue 10, pp 4033–4044 | Cite as

Tunable mechanical properties through texture control of polycrystalline additively manufactured materials using adjoint-based gradient optimization

  • Grace X. Gu
  • Markus J. Buehler
Original Paper
  • 67 Downloads

Abstract

Polycrystalline materials can be characterized by the preferred orientation of grains within a material, otherwise known as texture. It has been shown that texture can affect a wide range of mechanical properties in metallic materials, including elastic moduli, yield stress, strain hardening, and fracture toughness. Recent advances in additive manufacturing of metallic materials allow for controlling the spatial variation of texture and thus provide a path forward for controlling material properties through additive manufacturing. This paper investigates the benefits, in terms of mechanical performance, of varying texture spatially. We examine the material properties of a hole in a plate under load and use an adjoint-based gradient optimization algorithm coupled with a finite element solver. The method of adjoints allows for efficient calculation of design problems in a large variable space, reducing overall computational cost. As a first step to general texture optimization, we consider the idealized case of a pure fiber texture where the homogenized properties are transversely isotropic. In this special case, the only spatially varying design variables are the angles that describe the orientation of the homogenized material at each point within the structure. Material angles for both a spatially homogeneous and a spatially heterogeneous material are optimized for quantities of interest, such as compliance and von Mises stress. Additionally, the combined effects of elasticity tensor and material orientation on optimized structures are explored, as the additive manufacturing processes can potentially vary both. This work paves a way forward to design metallic materials with tunable mechanical properties at the microstructure level and is readily adapted to other materials.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors would like to acknowledge Sandia National Labs for supporting this research. The authors would also like to thank Judy Brown, Joseph Bishop, and Eliot Fang for their immensely helpful discussions. Additionally, the authors would like to thank Sam Raymond and Maysam Bandpay for their insightful discussions. Authors also appreciate support from the NDSEG Fellowship.

References

  1. 1.
    Gibson, I., Rosen, D., Stucker, B.: Additive Manufacturing Technologies: Design for additive manufacturing. Springer, Boston, MA (2010)CrossRefGoogle Scholar
  2. 2.
    Gibson, I., Rosen, D.W., Stucker, B.: Additive Manufacturing Technologies. Springer, Berlin (2010)CrossRefGoogle Scholar
  3. 3.
    Goehrke, S.A.: Metal 3D Printing with Machine Learning: GE Tells Us About Smarter Additive Manufacturing. https://3dprint.com/191973/3d-printing-machine-learning-ge/ (2017)
  4. 4.
    Murr, L.E., Martinez, E., Amato, K.N., Gaytan, S.M., Hernandez, J., Ramirez, D.A., Shindo, P.W., Medina, F., Wicker, R.B.: Fabrication of metal and alloy components by additive manufacturing: examples of 3D materials science. J. Mater. Res. Technol. 1(1), 42–54 (2012)CrossRefGoogle Scholar
  5. 5.
    Gorji, M.B., Tancogne-Dejean, T., Mohr, D.: Heterogeneous random medium plasticity and fracture model of additively-manufactured Ti–6Al–4V. Acta Materialia 148, 442–455 (2018)CrossRefGoogle Scholar
  6. 6.
    Compton, B.G., Lewis, J.A.: 3D-printing of lightweight cellular composites. Adv. Mater. 26(34), 5930–5935 (2014)CrossRefGoogle Scholar
  7. 7.
    Gu, G.X., Takaffoli, M., Buehler, M.J.: Hierarchically enhanced impact resistance of bioinspired composites. Adv. Mater. 29(28), 1700060 (2017)CrossRefGoogle Scholar
  8. 8.
    Jared, B.H., Aguilo, M.A., Beghini, L.L., Boyce, B.L., Clark, B.W., Cook, A., Kaehr, B.J., Robbins, J.: Additive manufacturing: toward holistic design. Scr. Mater. 135, 141–147 (2017)CrossRefGoogle Scholar
  9. 9.
    Zegard, T., Paulino, G.H.: Bridging topology optimization and additive manufacturing. Struct. Multidiscip. Optim. 53(1), 175–192 (2016)CrossRefGoogle Scholar
  10. 10.
    Gu, G.X., Libonati, F., Wettermark, S., Buehler, M.J.: Printing nature: unraveling the role of nacre’s mineral bridges. J. Mech. Behav. Biomed. Mater. 76, 135–144 (2017)CrossRefGoogle Scholar
  11. 11.
    Gu, G.X., Su, I., Sharma, S., Voros, J.L., Qin, Z., Buehler, M.J.: Three-dimensional-printing of bio-inspired composites. J. Biomech. Eng. 138(2), 021006 (2016)CrossRefGoogle Scholar
  12. 12.
    Das, S., Bourell, D.L., Babu, S.: Metallic materials for 3D printing. MRS Bull. 41(10), 729–741 (2016)CrossRefGoogle Scholar
  13. 13.
    Ding, Y., Muñiz-Lerma, J., Trask, M., Chou, S., Walker, A., Brochu, M.: Microstructure and mechanical property considerations in additive manufacturing of aluminum alloys. MRS Bull. 41(10), 745–751 (2016)CrossRefGoogle Scholar
  14. 14.
    Raghavan, N., Dehoff, R., Pannala, S., Simunovic, S., Kirka, M., Turner, J., Carlson, N., Babu, S.S.: Numerical modeling of heat-transfer and the influence of process parameters on tailoring the grain morphology of IN718 in electron beam additive manufacturing. Acta Mater. 112, 303–314 (2016)CrossRefGoogle Scholar
  15. 15.
    Makiewicz, K., Babu, S., Keller, M., Chaudhary, A.: Microstructure evolution during laser additive manufacturing of Ti6Al4V Alloys. In: Proceedings of International Conference on Trends in Welding Research, Chicago, IL (2012)Google Scholar
  16. 16.
    Dinda, G., Dasgupta, A., Mazumder, J.: Texture control during laser deposition of nickel-based superalloy. Scr. Mater. 67(5), 503–506 (2012)CrossRefGoogle Scholar
  17. 17.
    Dehoff, R., Kirka, M., Sames, W., Bilheux, H., Tremsin, A., Lowe, L., Babu, S.: Site specific control of crystallographic grain orientation through electron beam additive manufacturing. Mater. Sci. Technol. 31(8), 931–938 (2015)CrossRefGoogle Scholar
  18. 18.
    Brackett, D., Ashcroft, I., Hague, R.: Topology optimization for additive manufacturing. In: Proceedings of the Solid Freeform Fabrication Symposium, Austin, TX, USA, pp. 348–362 (2011)Google Scholar
  19. 19.
    Gaynor, A.T., Meisel, N.A., Williams, C.B., Guest, J.K.: Multiple-material topology optimization of compliant mechanisms created via PolyJet three-dimensional printing. J. Manuf. Sci. Eng. 136(6), 061015 (2014)CrossRefGoogle Scholar
  20. 20.
    Gu, G.X., Chen, C.-T., Buehler, M.J.: De novo composite design based on machine learning algorithm. Extreme Mech. Lett. 18, 19–28 (2017)CrossRefGoogle Scholar
  21. 21.
    Gu, G.X., Dimas, L., Qin, Z., Buehler, M.J.: Optimization of composite fracture properties: method, validation, and applications. J. Appl. Mech. 83(7), 071006 (2016)CrossRefGoogle Scholar
  22. 22.
    Gu, G.X., Wettermark, S., Buehler, M.J.: Algorithm-driven design of fracture resistant composite materials realized through additive manufacturing. Addit. Manuf. 17, 47–54 (2017)CrossRefGoogle Scholar
  23. 23.
    Soremekun, G., Gürdal, Z., Haftka, R., Watson, L.: Composite laminate design optimization by genetic algorithm with generalized elitist selection. Comput. Struct. 79(2), 131–143 (2001)CrossRefGoogle Scholar
  24. 24.
    Bendsøe, M.P., Sigmund, O., Bendsøe, M.P., Sigmund, O.: Topology Optimization by Distribution of Isotropic Material. Springer, Berlin (2004)CrossRefGoogle Scholar
  25. 25.
    Buhl, T., Pedersen, C.B., Sigmund, O.: Stiffness design of geometrically nonlinear structures using topology optimization. Struct. Multidiscip. Optim. 19(2), 93–104 (2000)CrossRefGoogle Scholar
  26. 26.
    Eschenauer, H.A., Olhoff, N.: Topology optimization of continuum structures: a review. Appl. Mech. Rev. 54(4), 331–390 (2001)CrossRefGoogle Scholar
  27. 27.
    Larsen, U.D., Signund, O., Bouwsta, S.: Design and fabrication of compliant micromechanisms and structures with negative Poisson’s ratio. J. Microelectromech. Syst. 6(2), 99–106 (1997)CrossRefGoogle Scholar
  28. 28.
    Le Riche, R., Haftka, R.T.: Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm. AIAA J. 31(5), 951–956 (1993)CrossRefGoogle Scholar
  29. 29.
    Lin, C.-C., Lee, Y.-J.: Stacking sequence optimization of laminated composite structures using genetic algorithm with local improvement. Compos. Struct. 63(3), 339–345 (2004)CrossRefGoogle Scholar
  30. 30.
    Multiphysics, C.O.M.S.O.L.: Modeling Software, User Manual (2016)Google Scholar
  31. 31.
    Brown, J.A., Bishop, J.E.: Quantifying the impact of material-model error on macroscale quantities-of-interest using multiscale a posteriori error-estimation techniques. MRS Adv. 1(40), 2789–2794 (2016)CrossRefGoogle Scholar
  32. 32.
    Bower, A.F.: Constitutive models: relations between stress and strain, chap. 3. In: Applied Mechanics of Solids, pp. 91–93 (2009)Google Scholar
  33. 33.
    Svanberg, K.: The method of moving asymptotes—a new method for structural optimization. Int. J. Numer. Methods Eng. 24(2), 359–373 (1987)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Choi, K.K., Kim, N.-H.: Structural Sensitivity Analysis and Optimization 1: Linear Systems. Springer, New York (2006)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations