Functionally Graded Metallic Materials by Directed Energy Deposition Additive Manufacturing: Computational Design, Fabrication and Validation

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The layer-by-layer process of additive manufacturing enables the controlled variation of material compositions, and therefore, properties, as a function of locations in a fabricated part. Such a unique capability has the potential to drastically transform the engineering design paradigm, inspiring innovative structures with spatially tailored multi-functional properties (e.g., physical, mechanical and thermal, etc.), which are strongly desired in many applications such as turbine blades. However, the complexity of phase formation resulted from the simultaneous deposition of disparate materials during additive manufacturing is least understood and hinders the ability to not only design, but also successfully produce materials of required functional gradients. This award supports fundamental research aimed at enabling the design and fabrication of functionally graded metallic materials using the laser powder-fed directed energy deposition process. The present research endeavors to develop comprehensive understanding of phase formation and transformations during layer-wise making of multi-component systems using integrated computational and experimental tools. In addition to its potential to reignite U.S. manufacturing, additive manufacturing’s power in tailoring properties within complex three-dimensional components will also significantly expand the design space and yield structures with enhanced integrity. The multidisciplinary nature of the research methodologies, along with crafted educational and outreach activities, will impact workforce development through the engagement of graduate and undergraduate students as well as the broader manufacturing community.The objective of the present research is to uncover the underlying mechanism of phase formation during the fabrication, via directed energy deposition additive manufacturing, of functionally graded metallic materials. The research will include the construction of a new multi-component thermodynamic database covering the complete compositional space of interest using novel high throughput first-principles calculations, deep neural network machine learning models, and high throughput thermodynamic modeling tools with uncertainty quantification. With this database, a combination of thermodynamic phase equilibrium calculations and kinetic phase transformation simulations will be used for phases formation predictions. The models will be applied to design compositional pathways between two metallic alloys, in a nonlinear fashion, for successful gradients in order to, e.g., avoid detrimental intermetallic phases. The designed functionally graded materials will be realized using a directed energy deposition machine and blending two different powders (titanium alloy and iron-nickel alloy) varying along the build height according to the design. Further, the compositions, microstructures and mechanical properties of fabricated parts will be thoroughly characterized and quantitatively compared with simulation results to refine the computational models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.