|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||Design of High Entropy Superalloy FeNiCrCoAl using Molecular Dynamics, Computational Thermodynamics and Machine Learning
||Tria Laksana Achmad, Fauzi Teja Sukma, Putri Aisyana Wibowo
|On-Site Speaker (Planned)
||Tria Laksana Achmad
The development of High Entropy Superalloy (HESA) with superior mechanical properties and affordable raw material raises the possibility of replacing superalloys. Designing a HESA frequently involves expensive and time-consuming processes of experimental. Even with the growth of computational studies, it still complicated to model multicomponent alloy systems. This study focuses on the compositional design simulation of HESA FeNiCrCoAl on lattice parameters, stacking fault energy (SFE), and compression strength using molecular dynamics (MD). We also propose a new approach to predict SFE using extensive data analysis by leveraging machine learning and computational thermodynamics. Then it is possible to explore the high-dimensional composition space much more efficiently. Increasing Al, Cr, and Co will decrease the SFE, while increasing Ni will increase the SFE. An optimal design guide for achieving desired SFE values: Ni (18-25 at%), Cr (24-30 at%), Al (5-15 at%), Co (20-35 at%), and Fe (20-35 at%). This work provides valuable insights into HESA mechanical behavior to improve creep resistance.
||Planned: Metallurgical and Materials Transactions