  During powder production, the pre-alloyed powder composition often deviates
from the target composition leading to undesirable properties of additive
manufacturing (AM) components. Therefore, we developed a method to perform
high-throughput calculation and uncertainty quantification by using a
CALPHAD-based ICME framework (CALPHAD: calculations of phase diagrams, ICME:
integrated computational materials engineering) to optimize the composition,
and took the high-strength low-alloy steel (HSLA) as a case study. We analyzed
the process-structure-property relationships for 450,000 compositions around
the nominal composition of HSLA-115. Properties that are critical for the
performance, such as yield strength, impact transition temperature, and
weldability, were evaluated to optimize the composition. With the same
uncertainty as the initial composition, an optimized average composition has
been determined, which increased the probability of achieving successful AM
builds by 44.7%. The present strategy is general and can be applied to other
alloy composition optimization to expand the choices of alloy for additive
manufacturing. Such a method also calls for high-quality CALPHAD databases and
predictive ICME models.
