LAUSR

Abstract Typically, the development of materials is intimately tied to the concurrent development of manufacturing technologies especially suitable to that material. In the case of metal additive manufacturing (AM), in contrast, the alloys typically used a feedstock were originally developed having other traditional manufacturing technologies in mind. Since metal AM processes involve fundamentally different physics, it is common to encounter many challenges when processing these materials such as high susceptibility to defects and microstructure inconsistencies. Recent research directions call for developing new materials and alloys specifically for AM. This poses a new and significant challenge: how can we determine the optimal processing recipes for an alloy not previously investigated. This is a typically time-consuming and expensive endeavor and, in this work, we propose an efficient framework to efficiently and effectively determine the processing parameter window of a given alloy considered as potential feedstock for AM. The framework integrates design of experiments, physics-based simulation, uncertainty analysis and fabrication and characterization in order to determine the bounding region in the manufacturing space resulting in near full density, defect-free parts. The proposed framework is developed aiming at maximizing its efficiency, accessibility and practicality. We validate the proposed framework and demonstrate its robustness using three model materials, two of which have not been previously reported.