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Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight
The application of high-strength steel plates can reduce ship weight, and the saddle plate is one of the most common types of double-curved hull plates. To fill the research gap regarding high-strength steel saddle plates, two prediction models are established here to predict deformation in saddle p...
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/PMC10488460/ https://www.ncbi.nlm.nih.gov/pubmed/37687721 http://dx.doi.org/10.3390/ma16176028 |
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author | Wang, Shun Dai, Jinliang Xu, Zhikang Wang, Ji Li, Rui Wang, Jiayan |
author_facet | Wang, Shun Dai, Jinliang Xu, Zhikang Wang, Ji Li, Rui Wang, Jiayan |
author_sort | Wang, Shun |
collection | PubMed |
description | The application of high-strength steel plates can reduce ship weight, and the saddle plate is one of the most common types of double-curved hull plates. To fill the research gap regarding high-strength steel saddle plates, two prediction models are established here to predict deformation in saddle plate forming. Deflection is a key parameter reflecting the overall deformation of a curved plate. Therefore, first of all, the influencing factors of the line heating of high-strength steel saddle plates were analyzed. The influence of plate geometric parameters and forming parameters on deflection was researched. Second, a multiple linear regression model between deflection and the geometric parameters and forming parameters of high-strength steel saddle plates was established. Finally, to solve the problem of a large error in the multivariate regression model for extrapolation, an intelligent prediction program for deflection based on a support vector machine (SVM) was developed using the Python language. The results show that the error of the multiple regression model was less than 5% for data interpolation. The error of the intelligent prediction model for deflection was less than 5% for data extrapolation. This research can provide data support for the automatic forming of marine saddle plates. |
format | Online Article Text |
id | pubmed-10488460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104884602023-09-09 Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight Wang, Shun Dai, Jinliang Xu, Zhikang Wang, Ji Li, Rui Wang, Jiayan Materials (Basel) Article The application of high-strength steel plates can reduce ship weight, and the saddle plate is one of the most common types of double-curved hull plates. To fill the research gap regarding high-strength steel saddle plates, two prediction models are established here to predict deformation in saddle plate forming. Deflection is a key parameter reflecting the overall deformation of a curved plate. Therefore, first of all, the influencing factors of the line heating of high-strength steel saddle plates were analyzed. The influence of plate geometric parameters and forming parameters on deflection was researched. Second, a multiple linear regression model between deflection and the geometric parameters and forming parameters of high-strength steel saddle plates was established. Finally, to solve the problem of a large error in the multivariate regression model for extrapolation, an intelligent prediction program for deflection based on a support vector machine (SVM) was developed using the Python language. The results show that the error of the multiple regression model was less than 5% for data interpolation. The error of the intelligent prediction model for deflection was less than 5% for data extrapolation. This research can provide data support for the automatic forming of marine saddle plates. MDPI 2023-09-01 /pmc/articles/PMC10488460/ /pubmed/37687721 http://dx.doi.org/10.3390/ma16176028 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 Wang, Shun Dai, Jinliang Xu, Zhikang Wang, Ji Li, Rui Wang, Jiayan Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight |
title | Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight |
title_full | Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight |
title_fullStr | Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight |
title_full_unstemmed | Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight |
title_short | Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight |
title_sort | deflection intelligent prediction for high-strength steel saddle plate forming applicable to reducing ship weight |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488460/ https://www.ncbi.nlm.nih.gov/pubmed/37687721 http://dx.doi.org/10.3390/ma16176028 |
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