Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/135281
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DC Field | Value | Language |
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dc.contributor.author | Zhu, W. | - |
dc.contributor.author | Huo, W. | - |
dc.contributor.author | Wang, S. | - |
dc.contributor.author | Wang, X. | - |
dc.contributor.author | Ren, K. | - |
dc.contributor.author | Tan, S. | - |
dc.contributor.author | Fang, F. | - |
dc.contributor.author | Xie, Z. | - |
dc.contributor.author | Jiang, J. | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Journal of Materials Research and Technology, 2022; 18:800-809 | - |
dc.identifier.issn | 2238-7854 | - |
dc.identifier.issn | 2214-0697 | - |
dc.identifier.uri | https://hdl.handle.net/2440/135281 | - |
dc.description.abstract | High-entropy alloys (HEAs) represent prospective applications considering their outstanding mechanical properties. The properties in HEAs can be affected by the phase structure. Artificial neural network (ANN) is a promising machine learning approach for predicting the phases of HEAs. In this work, a deep neural network (DNN) structure using a residual network (RESNET) is proposed for the phase formation prediction of HEAs. It shows a high overall accuracy of 81.9%. Compared it with machine learning models, e.g., ANN and conventional DNN, its Micro-F1 score highlights the advantages of phase prediction of HEAs. It can remarkably prevent network degradation and improve the algorithm accuracy. It delivers a new path to develop the phase formation prediction model using deep learning, which can be of universal relevance in assisting the design of the HEAs with novel chemical compositions. | - |
dc.description.statementofresponsibility | Wenhan Zhu, Wenyi Huo, Shiqi Wang, Xu Wang, Kai Ren, Shuyong Tan, Feng Fang, Zonghan Xie, Jianqing Jiang | - |
dc.language.iso | en | - |
dc.publisher | Elsevier BV | - |
dc.rights | © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). | - |
dc.source.uri | http://dx.doi.org/10.1016/j.jmrt.2022.01.172 | - |
dc.subject | High-entropy alloys | - |
dc.subject | Phase Phase formation | - |
dc.subject | Deep neural network | - |
dc.subject | Residual network | - |
dc.title | Phase formation prediction of high-entropy alloys: a deep learning study | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.jmrt.2022.01.172 | - |
dc.relation.grant | ARC | - |
pubs.publication-status | Published | - |
Appears in Collections: | Mechanical Engineering publications |
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File | Description | Size | Format | |
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hdl_135281.pdf | Published version | 2.59 MB | Adobe PDF | View/Open |
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