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Code for "Design and Selection of High Entropy Alloys for Hardmetal Matrix Applications using a Coupled Machine Learning and CALPHAD Methodology"

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posted on 2024-02-25, 23:52 authored by Joshua BerryJoshua Berry, Katerina ChristofidouKaterina Christofidou, Iain ToddIain Todd, Robert Snell, Lewis OwenLewis Owen, Olivier Messé, Magnus Anderson

Abstract: This study aimed to utilise a combined Machine Learning (ML) and CALculations of PHAse Diagrams (CALPHAD) methodology to design hardmetal matrix phases for metal forming applications that could serve as the basis for carbide reinforcement. The vast compositional space that High Entropy Alloys (HEAs) occupy, offers a promising avenue to satisfy the application design criteria of wear resistance and ductility. To efficiently explore this space, random forest ML models are constructed and trained from publicly available experimental HEA databases to make phase constitution and hardness predictions. Interrogation of the ML models constructed revealed accuracies > 78.7% and mean absolute error of 66.1 HV for phase and hardness predictions. Six promising alloy compositions, extracted from the ML predictions and CALPHAD calculations, were experimentally fabricated and tested. The hardness predictions are found to be systematically under and over predicted depending on the alloy microstructure. In parallel, the phase classification models were found to lack sensitivity towards additional intermetallic phase formation. Despite the discrepancies identified between ML and experimental results, the fabricated compositions showed promise for further experimental evaluation. These discrepancies were believed to be directly associated with the available databases but importantly have highlighted several avenues for both ML and database development.

Funding

EPSRC and SFI Centre for Doctoral Training in Advanced Metallic Systems: Metallurgical Challenges for the Digital Manufacturing Environment

Engineering and Physical Sciences Research Council

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EPSRC-SFI Centre for Doctoral Training in Advanced Metallic Systems: Metallurgical Challenges for the Digital Manufacturing Environment

Science Foundation Ireland

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Sir Henry Royce InsStitute - recurrent grant

Engineering and Physical Sciences Research Council

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The Royce: Capitalising on the investment

Engineering and Physical Sciences Research Council

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Sir Henry Royce Institute -Sheffield Equipment

Engineering and Physical Sciences Research Council

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Sir Henry Royce Institute - Sheffield Build

Engineering and Physical Sciences Research Council

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    Department of Materials Science and Engineering

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