Tunable mechanical properties through texture control of polycrystalline additively manufactured materials using adjoint-based gradient optimization
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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.
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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.
- 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)
- 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.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
- 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
- 30.Multiphysics, C.O.M.S.O.L.: Modeling Software, User Manual (2016)Google Scholar
- 32.Bower, A.F.: Constitutive models: relations between stress and strain, chap. 3. In: Applied Mechanics of Solids, pp. 91–93 (2009)Google Scholar
- 34.Choi, K.K., Kim, N.-H.: Structural Sensitivity Analysis and Optimization 1: Linear Systems. Springer, New York (2006)Google Scholar