Experimentally validated and empirically compared machine learning approach for predicting yield strength of additively manufactured multi-principal element alloys from Co-Cr-Fe-Mn-Ni system

TL;DR

This paper presents a machine learning approach to predict the yield strength of additively manufactured multi-principal element alloys, offering a faster alternative to microstructural analysis and empirical methods.

Contribution

It introduces a validated ML model that correlates composition and printing parameters with yield strength, demonstrating comparable accuracy to traditional microstructure-based predictions.

Findings

ML model achieved R2 of 0.84 in yield strength prediction

Printing parameters like scan velocity and laser power significantly influence yield strength

Validated predictions on two new alloy compositions

Abstract

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.