{"title":"Machine Learning-Based Prediction of High-Entropy Alloy Hardness: Design and Experimental Validation of Superior Hardness","authors":"Xiaomin Li, Jian Sun, Xizhang Chen","doi":"10.1007/s12666-024-03450-5","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":23224,"journal":{"name":"Transactions of The Indian Institute of Metals","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of The Indian Institute of Metals","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s12666-024-03450-5","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Materials Science","Score":null,"Total":0}
引用次数: 0
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.
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.