Journal
SCRIPTA MATERIALIA
Volume 156, Issue -, Pages 120-123Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.scriptamat.2018.07.024
Keywords
Phase diagram; Modeling; Simulation; Thermodynamics; Machine learning
Categories
Funding
- European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant [656249]
- EPSRC [EP/L025213/1] Funding Source: UKRI
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A multi-objective optimisation genetic algorithm combining solid solution hardening (SSH) and thermodynamic modelling (CALPHAD) with data mining is used to design high entropy alloys (HEAs). The approach searches for the best compromise between single-phase stability, SSH and density. Thousands of Pareto-optimal base-centred cubic (BCC) HEAs are designed. Al35Cr35Mn8Mo5Ti17 (at.%) is chosen for experimental validation. The alloy was cast and characterised. Its microstructure consists of large grains of a single disordered solid solution displaying a Vickers hardness of 6.45 GPa (658 HV) and a density below 5.5 g/cm(3); uniquely combining exceptional hardness with medium density. (C) 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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