  Traditionally, yield strength prediction relies on detailed and
resource-intensive microstructural characterization combined with empirical
equations. However, quantifying microstructural feature length scales for novel
processes like additive manufacturing, which involves inhomogeneous
hierarchical features, poses a challenge. The lack of accurate material
constants for broader composition ranges further limits empirical predictions.
This study proposes an alternative machine learning (ML) approach for
predicting the yield strength of additively manufactured (AM) multi-principal
element alloys (MPEAs) from the Co-Cr-Fe-Mn-Ni system by correlating
composition, printing parameters, and testing conditions. The best-performing
ML model achieved an R2 of 0.84, comparable to that achieved using
microstructural detail-driven empirical strengthening contributions. The
validity of the ML approach was further confirmed by printing and testing two
compositions (one novel and one from the dataset). This data-driven approach
directly relates yield strength to initial printing parameters, highlighting
their significance and individual effects, such as scan velocity's direct
impact and laser power's inverse impact on yield strength. This demonstrates
ML's potential to guide AM processes, reducing the need for iterative
experiments and enabling rapid exploration of compositional and printing spaces
to achieve desired properties.
