Multi-principal element alloy discovery using directed energy deposition and machine learning

TL;DR

This paper demonstrates a high-throughput approach combining additive manufacturing, automated characterization, and active machine learning to rapidly discover and optimize multi-principal element alloys with enhanced strength.

Contribution

It introduces a novel integrated method for alloy discovery that accelerates synthesis, characterization, and predictive modeling within a large compositional space.

Findings

Over 100 alloy compositions synthesized in a week

Achieved compositional control within +/-5 at%

Discovered alloys with significantly improved strength

Abstract

Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy design. Additive manufacturing via directed energy deposition is demonstrated as a high-throughput technique for synthesizing alloys in the Cr-Fe-Mn-Ni quaternary system. More than 100 compositions are synthesized in a week, exploring a broad range of compositional space. Uniform compositional control to within +/-5 at% is achievable. The rapid synthesis is combined with conjoint sample heat treatment (25 samples vs 1 sample), and automated characterization including X-ray diffraction, energy-dispersive X-ray spectroscopy, and nano-hardness measurements. The datasets of measured properties are then used for a predictive strengthening model using an active machine learning algorithm that balances exploitation and exploration. A learned parameter that represents lattice distortion is trained using the alloy compositions. This combination of rapid synthesis, characterization, and active learning model results in new alloys that are significantly stronger than previous investigated alloys.