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Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel
Evaluating the condition of a Hadfield steel crossing nose using existing inspection methods is subject to accessibility and geographical constraints. Thus, the use of conditional monitoring techniques to complement the existing inspection methods has become increasingly necessary. This paper focuse...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383830/ https://www.ncbi.nlm.nih.gov/pubmed/37514854 http://dx.doi.org/10.3390/s23146561 |
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author | Shi, Shengrun Wu, Guiyi Chen, Hui Zhang, Shuyan |
author_facet | Shi, Shengrun Wu, Guiyi Chen, Hui Zhang, Shuyan |
author_sort | Shi, Shengrun |
collection | PubMed |
description | Evaluating the condition of a Hadfield steel crossing nose using existing inspection methods is subject to accessibility and geographical constraints. Thus, the use of conditional monitoring techniques to complement the existing inspection methods has become increasingly necessary. This paper focuses on the study of acoustic emission (AE) behaviour and its correlation with fatigue crack growth in Hadfield steel during bending fatigue tests. The probability density function for acoustic emission parameters was analysed based on the power law distribution. The results show that a sharp increase in the moving average and cumulative sum of the AE parameter can give early warning against the final failure of Hadfield steel. Two parts (Part 1 and Part 2) can be identified using the change in the slope of duration rate (dD/dN) vs. ΔK plot during the stable fatigue crack growth (FCG) process where Paris’s law is valid. The fitted power law exponent of AE parameters is smaller in Part 2 than in Part 1. The novelty of this research lies in the use of the fitted power law distribution of AE parameters for monitoring fatigue damage evolution in Hadfield steel, unlike existing AE fatigue monitoring methodology, which relies solely on the analysis of AE parameter trends. |
format | Online Article Text |
id | pubmed-10383830 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103838302023-07-30 Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel Shi, Shengrun Wu, Guiyi Chen, Hui Zhang, Shuyan Sensors (Basel) Article Evaluating the condition of a Hadfield steel crossing nose using existing inspection methods is subject to accessibility and geographical constraints. Thus, the use of conditional monitoring techniques to complement the existing inspection methods has become increasingly necessary. This paper focuses on the study of acoustic emission (AE) behaviour and its correlation with fatigue crack growth in Hadfield steel during bending fatigue tests. The probability density function for acoustic emission parameters was analysed based on the power law distribution. The results show that a sharp increase in the moving average and cumulative sum of the AE parameter can give early warning against the final failure of Hadfield steel. Two parts (Part 1 and Part 2) can be identified using the change in the slope of duration rate (dD/dN) vs. ΔK plot during the stable fatigue crack growth (FCG) process where Paris’s law is valid. The fitted power law exponent of AE parameters is smaller in Part 2 than in Part 1. The novelty of this research lies in the use of the fitted power law distribution of AE parameters for monitoring fatigue damage evolution in Hadfield steel, unlike existing AE fatigue monitoring methodology, which relies solely on the analysis of AE parameter trends. MDPI 2023-07-20 /pmc/articles/PMC10383830/ /pubmed/37514854 http://dx.doi.org/10.3390/s23146561 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shi, Shengrun Wu, Guiyi Chen, Hui Zhang, Shuyan Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel |
title | Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel |
title_full | Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel |
title_fullStr | Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel |
title_full_unstemmed | Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel |
title_short | Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel |
title_sort | acoustic emission monitoring of fatigue crack growth in hadfield steel |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383830/ https://www.ncbi.nlm.nih.gov/pubmed/37514854 http://dx.doi.org/10.3390/s23146561 |
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