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Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars
In high-strength rebar, the various microstructures obtained by the Tempcore process and the addition of V have a complex effect on the strength improvement of rebar. This study investigated the mechanism of strengthening of high-strength Tempcore rebars upon the addition of vanadium through artific...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9181190/ https://www.ncbi.nlm.nih.gov/pubmed/35683077 http://dx.doi.org/10.3390/ma15113781 |
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author | Choi, Woonam Won, Sungbin Kim, Gil-Su Kang, Namhyun |
author_facet | Choi, Woonam Won, Sungbin Kim, Gil-Su Kang, Namhyun |
author_sort | Choi, Woonam |
collection | PubMed |
description | In high-strength rebar, the various microstructures obtained by the Tempcore process and the addition of V have a complex effect on the strength improvement of rebar. This study investigated the mechanism of strengthening of high-strength Tempcore rebars upon the addition of vanadium through artificial neural network (ANN) modelling. Various V contents (0.005, 0.072 and 0.14 wt.%) were investigated, and a large amount of bainite and V(C, N) were precipitated in the core of the Tempcore rebar in the high-V specimens. In addition, as the V content increased, the number of these fine precipitates (10–30 nm) increased. The precipitation strengthening proposed by the Ashby–Orowan model is a major contributing factor to the yield-strength increase (35 MPa) of the Tempcore rebar containing 0.140 wt.% V. The ANN model was developed to predict the yield and tensile strengths of Tempcore rebar after the addition of various amounts of V and self-tempering at various temperatures, and it showed high reproducibility compared to the experimental values (R-square was 93% and the average relative error was 2.6%). ANN modelling revealed that the yield strength of the Tempcore rebar increased more significantly with increasing V content (0.01–0.2 wt.%.) at relatively high self-tempering temperatures (≥530 °C). These results provide guidelines for selecting the optimal V content and process conditions for manufacturing high-strength Tempcore rebars. |
format | Online Article Text |
id | pubmed-9181190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91811902022-06-10 Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars Choi, Woonam Won, Sungbin Kim, Gil-Su Kang, Namhyun Materials (Basel) Article In high-strength rebar, the various microstructures obtained by the Tempcore process and the addition of V have a complex effect on the strength improvement of rebar. This study investigated the mechanism of strengthening of high-strength Tempcore rebars upon the addition of vanadium through artificial neural network (ANN) modelling. Various V contents (0.005, 0.072 and 0.14 wt.%) were investigated, and a large amount of bainite and V(C, N) were precipitated in the core of the Tempcore rebar in the high-V specimens. In addition, as the V content increased, the number of these fine precipitates (10–30 nm) increased. The precipitation strengthening proposed by the Ashby–Orowan model is a major contributing factor to the yield-strength increase (35 MPa) of the Tempcore rebar containing 0.140 wt.% V. The ANN model was developed to predict the yield and tensile strengths of Tempcore rebar after the addition of various amounts of V and self-tempering at various temperatures, and it showed high reproducibility compared to the experimental values (R-square was 93% and the average relative error was 2.6%). ANN modelling revealed that the yield strength of the Tempcore rebar increased more significantly with increasing V content (0.01–0.2 wt.%.) at relatively high self-tempering temperatures (≥530 °C). These results provide guidelines for selecting the optimal V content and process conditions for manufacturing high-strength Tempcore rebars. MDPI 2022-05-25 /pmc/articles/PMC9181190/ /pubmed/35683077 http://dx.doi.org/10.3390/ma15113781 Text en © 2022 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 Choi, Woonam Won, Sungbin Kim, Gil-Su Kang, Namhyun Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars |
title | Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars |
title_full | Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars |
title_fullStr | Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars |
title_full_unstemmed | Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars |
title_short | Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars |
title_sort | artificial neural network modelling of the effect of vanadium addition on the tensile properties and microstructure of high-strength tempcore rebars |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9181190/ https://www.ncbi.nlm.nih.gov/pubmed/35683077 http://dx.doi.org/10.3390/ma15113781 |
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