A First-Principles-Guided Framework for the Synthesis of Additive Manufacturing Alloys via Hydrogen Direct Reduction of Oxide Precursors

The production of specialized alloy powders for additive manufacturing (AM) requires high-quality materials which currently involve expensive and energy-consuming manufacturing methods. The research developed a computational framework which optimizes hydrogen direct reduction of mixed oxide powders to generate Fe-36Ni invar alloy powders for AM. The research applied Density Functional Theory (DFT) to study atomic-level surface reactions and oxide reduction diffusion mechanisms. The research findings allowed us to develop models which describe reaction kinetics, diffusion mechanisms, thermodynamic models for phase stability evaluation and Finite Element Analysis models for large-scale process behavior simulation. The integrated tools developed a predictive "process-to-property" model which can enable scientists to develop alloys and optimize manufacturing parameters to produce AM-compatible powders at reduced costs compared to conventional methods.