Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Deep learning-based phase prediction of high-entropy alloys

Al-Shibaany, Zeyad Yousif Abdoon, Alkhafaji, Nadia, Al-Obaidi, Yaser and Atiyah, Alaa Abdulhasan 2020. Deep learning-based phase prediction of high-entropy alloys. Presented at: The Ziggurat International Conference on Materials Science and Engineering (ZICMSE 2020), London, England, 5-6 October 2020. IOP Publishing: Conference Series / IOP Publishing, 012025. 10.1088/1757-899X/987/1/012025

[img] PDF - Published Version
Available under License Creative Commons Attribution.

Download (694kB)

Abstract

High-entropy alloys (HEAs) offer a new approach to the design of superior metallic materials, wherein alloys are based on multiple principal elements rather than just one. Deep Neural Networks (DNNs), machine learning tools that are efficiently used for prediction purposes, are transforming fields, from speech recognition to computational medicine. In this study, we extend DNN applications to the field phase prediction of high-entropy alloys. Using the built-in capabilities in TensorFlow and Keras, we train DNNs with different layers and numbers of neurons, achieving a 90% prediction accuracy. The DDN prediction model is examined in detail with different datasets to verify model robustness. Due to the high cost of HEAs and in order to save time, it is important to predict phases in order to design alloy composition. Through this study, we show trained DNNs to be a viable tool for predicting the phases of high-entropy alloys, where 90% phase prediction accuracy was achieved in this work.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: IOP Publishing: Conference Series / IOP Publishing
ISSN: 1757-8981
Date of First Compliant Deposit: 8 January 2021
Date of Acceptance: 6 November 2020
Last Modified: 20 Oct 2021 01:21
URI: http://orca.cardiff.ac.uk/id/eprint/137340

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics