Machine Learning-Based Prediction of High-Entropy Alloy Hardness: Design and Experimental Validation of Superior Hardness

IF 1.6 4区 材料科学 Q2 Materials Science
Xiaomin Li, Jian Sun, Xizhang Chen
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Abstract

The primary aim of this study is to predict the hardness of high entropy alloys and identify optimal alloy compositions with superior hardness through machine learning techniques. To enhance the accuracy of predictions, a dual-layer algorithmic machine learning model was employed and augmented with Shapley Additive Explanations (SHAP) analysis to increase the model’s interpretability. During model development, multiple machine learning algorithms were evaluated, and innovatively, a combination of the three most optimal model outcomes was incorporated into the prediction process, thus improving the accuracy of hardness predictions. Furthermore, using the Al–Co–Cr–Fe–Ni system as an example, an HEA with a predicted hardness of 776HV was identified from 820,000 datasets. This sample was fabricated using two different preparation techniques and subsequently validated through experimental testing.

Abstract Image

基于机器学习的高熵合金硬度预测:卓越硬度的设计与实验验证
本研究的主要目的是预测高熵合金的硬度,并通过机器学习技术找出具有优异硬度的最佳合金成分。为了提高预测的准确性,我们采用了双层算法机器学习模型,并通过夏普利加法解释(SHAP)分析来增强模型的可解释性。在模型开发过程中,对多种机器学习算法进行了评估,并创新性地将三个最佳模型结果的组合纳入了预测过程,从而提高了硬度预测的准确性。此外,以 Al-Co-Cr-Fe-Ni 系统为例,从 820,000 个数据集中识别出了预测硬度为 776HV 的 HEA。该样品采用两种不同的制备技术制造,随后通过实验测试进行了验证。
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来源期刊
Transactions of The Indian Institute of Metals
Transactions of The Indian Institute of Metals Materials Science-Metals and Alloys
CiteScore
2.60
自引率
6.20%
发文量
3
期刊介绍: Transactions of the Indian Institute of Metals publishes original research articles and reviews on ferrous and non-ferrous process metallurgy, structural and functional materials development, physical, chemical and mechanical metallurgy, welding science and technology, metal forming, particulate technologies, surface engineering, characterization of materials, thermodynamics and kinetics, materials modelling and other allied branches of Metallurgy and Materials Engineering. Transactions of the Indian Institute of Metals also serves as a forum for rapid publication of recent advances in all the branches of Metallurgy and Materials Engineering. The technical content of the journal is scrutinized by the Editorial Board composed of experts from various disciplines of Metallurgy and Materials Engineering. Editorial Advisory Board provides valuable advice on technical matters related to the publication of Transactions.
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