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Fatigue dataset of high-entropy alloys
Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be practically useful if it is prone to fatigue failures. To take the advantage of lately emerged high-entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259632/ https://www.ncbi.nlm.nih.gov/pubmed/35794115 http://dx.doi.org/10.1038/s41597-022-01368-5 |
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author | Chen, Shiyi Fan, Xuesong Steingrimsson, Baldur Xiong, Qingang Li, Weidong Liaw, Peter K. |
author_facet | Chen, Shiyi Fan, Xuesong Steingrimsson, Baldur Xiong, Qingang Li, Weidong Liaw, Peter K. |
author_sort | Chen, Shiyi |
collection | PubMed |
description | Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be practically useful if it is prone to fatigue failures. To take the advantage of lately emerged high-entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue database of HEAs from the literature reported until the beginning of 2022. The database is subdivided into three categories, i.e., low-cycle fatigue (LCF), high-cycle fatigue (HCF), and fatigue crack growth rate (FCGR), which contain 15, 23, and 28 distinct data records, respectively. Each data record in any of three categories is characteristic of a summary, which is comprised of alloy compositions, key fatigue properties, and additional information influential to, or interrelated with, fatigue (e.g., material processing history, phase constitution, grain size, uniaxial tensile properties, and fatigue testing conditions), and an individual dataset, which makes up the original fatigue testing curve. Some representative individual datasets in each category are graphically visualized. The dataset is hosted in an open data repository, Materials Cloud. |
format | Online Article Text |
id | pubmed-9259632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92596322022-07-08 Fatigue dataset of high-entropy alloys Chen, Shiyi Fan, Xuesong Steingrimsson, Baldur Xiong, Qingang Li, Weidong Liaw, Peter K. Sci Data Data Descriptor Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be practically useful if it is prone to fatigue failures. To take the advantage of lately emerged high-entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue database of HEAs from the literature reported until the beginning of 2022. The database is subdivided into three categories, i.e., low-cycle fatigue (LCF), high-cycle fatigue (HCF), and fatigue crack growth rate (FCGR), which contain 15, 23, and 28 distinct data records, respectively. Each data record in any of three categories is characteristic of a summary, which is comprised of alloy compositions, key fatigue properties, and additional information influential to, or interrelated with, fatigue (e.g., material processing history, phase constitution, grain size, uniaxial tensile properties, and fatigue testing conditions), and an individual dataset, which makes up the original fatigue testing curve. Some representative individual datasets in each category are graphically visualized. The dataset is hosted in an open data repository, Materials Cloud. Nature Publishing Group UK 2022-07-06 /pmc/articles/PMC9259632/ /pubmed/35794115 http://dx.doi.org/10.1038/s41597-022-01368-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Chen, Shiyi Fan, Xuesong Steingrimsson, Baldur Xiong, Qingang Li, Weidong Liaw, Peter K. Fatigue dataset of high-entropy alloys |
title | Fatigue dataset of high-entropy alloys |
title_full | Fatigue dataset of high-entropy alloys |
title_fullStr | Fatigue dataset of high-entropy alloys |
title_full_unstemmed | Fatigue dataset of high-entropy alloys |
title_short | Fatigue dataset of high-entropy alloys |
title_sort | fatigue dataset of high-entropy alloys |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259632/ https://www.ncbi.nlm.nih.gov/pubmed/35794115 http://dx.doi.org/10.1038/s41597-022-01368-5 |
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