| Abstract Scope |
Design of refractory high entropy alloys mandates processing methodologies to validate the alloy design schemes. Additive manufacturing (AM) uses computer-controlled, layer-wise powder deposition with a moving focal heat source. As a result, the synthesis method minimizes conventional constraints associated with refractory materials (such as molten alloy containment) while providing near-net-shape fabrication and microstructural refinement. However, predicting process parameters can be cumbersome and based upon trial-and-error. This study will present methodologies to use thermodynamic computational alloy design strategies coupled with fundamental dimensionless numbers (based upon heat and mass balances) for accelerated process parameter prediction. In addition, the use of artificial intelligence techniques for linking processing to characterized structure-property relationships will be illustrated. Overall, the developments enable feedback control, and coupled with side channels sensors or diagnostics, provide a pathway for autonomous control of processing for processing design in AM. Ultimately, synthesis-on-demand of any material is envisioned with compositional and microstructural control. |