Computational design of additively manufactured functionally graded materials for structural applications

Additive manufacturing (AM) has an enormous opportunity to become a leading technique for manufacturing high-value, mechanically optimized parts. Functionally graded materials (FMGs) prepared by additive manufacturing enable the use of composition as a spatial design parameter that can be used to control the stable phases in a part as well as transition between dissimilar materials to tailor the properties of materials as a function of position. However, a major challenge in the development of FGMs is the formation of brittle intermetallic and TCP phases and phase transformations that cause strain and cracking during the build process. Due to the number of degrees of freedom in the composition path between two dissimilar multicomponent alloys, it is not feasible to design viable FGM gradient paths that avoid forming detrimental phases using experiments alone. A thermodynamic database that covers the entire composition range in the Al-Cr-Fe-Mo-Ni-Ti-V can be used to develop FGMs between common materials used in AM such as commercially pure Ti, Ti-6Al-4V, stainless steels, Invar, and Inconel alloys. The database can be used to design multicomponent gradient paths between these alloys including any interlayers to facilitate the transition. Large, multicomponent thermodynamic databases across different materials systems have not traditionally been explored using CALPHAD modeling due to the challenges with evaluating the energies of experimentally inaccessible phases and with the maintenance and improvement of large databases. First-principles calculations based on density functional theory can be used to predict the thermodynamic properties of stable and metastable phases including phases with large amounts of non-stoichiometry that are present in structural materials, such as the sigma, mu, or Laves phase. The open-source Python software ESPEI has been developed and used to automate the modeling of binary thermodynamic systems and will be extended to generate CALPHAD parameterizations for higher order systems in this work, which will allow for the rapid optimization and reassessments of large, multicomponent thermodynamic databases. The Al-Cr-Fe-Mo-Ni-Ti-V database that will be created in this work will be able to be used for designing FGMs and other advanced structural materials relevant to NASA. The further development of ESPEI will allow this database to be maintained and continually improved so that it can be used in simulations requiring thermodynamic properties or phase relations, such as phase field modeling.

The open-source Python software ESPEI has been developed and used to automate the modeling of binary thermodynamic systems and will be extended to generate CALPHAD parameterizations for higher order systems in this work, which will allow for the rapid optimization and reassessments of large, multicomponent thermodynamic databases. The Al-Cr-Fe-Mo-Ni-Ti-V database that will be created in this work will be able to be used for designing FGMs and other advanced structural materials relevant to NASA. The further development of ESPEI will allow this database to be maintained and continually improved so that it can be used in simulations requiring thermodynamic properties or phase relations, such as phase field modeling.

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