  Designing alloys for additive manufacturing (AM) presents significant
opportunities. Still, the chemical composition and processing conditions
required for printability (ie., their suitability for fabrication via AM) are
challenging to explore using solely experimental means. In this work, we
develop a high-throughput (HTP) computational framework to guide the search for
highly printable alloys and appropriate processing parameters. The framework
uses material properties from state-of-the-art databases, processing
parameters, and simulated melt pool profiles to predict process-induced
defects, such as lack-of-fusion, keyholing, and balling. We accelerate the
printability assessment using a deep learning surrogate for a thermal model,
enabling a 1,000-fold acceleration in assessing the printability of a given
alloy at no loss in accuracy when compared with conventional physics-based
thermal models. We verify and validate the framework by constructing
printability maps for the CoCrFeMnNi Cantor alloy system and comparing our
predictions to an exhaustive 'in-house' database. The framework enables the
systematic investigation of the printability of a wide range of alloys in the
broader Co-Cr-Fe-Mn-Ni HEA system. We identified the most promising alloys that
were suitable for high-temperature applications and had the narrowest
solidification ranges, and that was the least susceptible to balling,
hot-cracking, and the formation of macroscopic printing defects. A new metric
for the global printability of an alloy is constructed and is further used for
the ranking of candidate alloys. The proposed framework is expected to be
integrated into ICME approaches to accelerate the discovery and optimization of
novel high-performance, printable alloys.
