Prediction of 316 stainless steel low-cycle fatigue life based on machine learning

The low-cycle fatigue life of 316 stainless steel is a significant basis for safety assessment. Usually, many factors affect the low-cycle fatigue life of stainless steel, and the relationship between the influencing factors and fatigue life is complicated and nonlinear. Therefore, it is hard to pre...

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Autores principales: Duan, Hongyan, Cao, Mengjie, Liu, Lin, Yue, Shunqiang, He, Hong, Zhao, Yingjian, Zhang, Zengwang, liu, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130168/
https://www.ncbi.nlm.nih.gov/pubmed/37185593
http://dx.doi.org/10.1038/s41598-023-33354-1
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author Duan, Hongyan
Cao, Mengjie
Liu, Lin
Yue, Shunqiang
He, Hong
Zhao, Yingjian
Zhang, Zengwang
liu, Yang
author_facet Duan, Hongyan
Cao, Mengjie
Liu, Lin
Yue, Shunqiang
He, Hong
Zhao, Yingjian
Zhang, Zengwang
liu, Yang
author_sort Duan, Hongyan
collection PubMed
description The low-cycle fatigue life of 316 stainless steel is a significant basis for safety assessment. Usually, many factors affect the low-cycle fatigue life of stainless steel, and the relationship between the influencing factors and fatigue life is complicated and nonlinear. Therefore, it is hard to predict fatigue life using the traditional empirical formula. Based on this, a machine learning algorithm is proposed. In this paper, based on the large amount of existing experimental data, machine learning methods are used to predict the low circumferential fatigue life of 316 stainless steel. The results show that the prediction accuracy of nu-SVR and ELM models is high and can meet engineering needs.
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spelling pubmed-101301682023-04-27 Prediction of 316 stainless steel low-cycle fatigue life based on machine learning Duan, Hongyan Cao, Mengjie Liu, Lin Yue, Shunqiang He, Hong Zhao, Yingjian Zhang, Zengwang liu, Yang Sci Rep Article The low-cycle fatigue life of 316 stainless steel is a significant basis for safety assessment. Usually, many factors affect the low-cycle fatigue life of stainless steel, and the relationship between the influencing factors and fatigue life is complicated and nonlinear. Therefore, it is hard to predict fatigue life using the traditional empirical formula. Based on this, a machine learning algorithm is proposed. In this paper, based on the large amount of existing experimental data, machine learning methods are used to predict the low circumferential fatigue life of 316 stainless steel. The results show that the prediction accuracy of nu-SVR and ELM models is high and can meet engineering needs. Nature Publishing Group UK 2023-04-25 /pmc/articles/PMC10130168/ /pubmed/37185593 http://dx.doi.org/10.1038/s41598-023-33354-1 Text en © The Author(s) 2023 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Duan, Hongyan
Cao, Mengjie
Liu, Lin
Yue, Shunqiang
He, Hong
Zhao, Yingjian
Zhang, Zengwang
liu, Yang
Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
title Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
title_full Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
title_fullStr Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
title_full_unstemmed Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
title_short Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
title_sort prediction of 316 stainless steel low-cycle fatigue life based on machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130168/
https://www.ncbi.nlm.nih.gov/pubmed/37185593
http://dx.doi.org/10.1038/s41598-023-33354-1
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