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ANFIS-based forming limit prediction of stainless steel 316 sheet metals
Effect of microstructure on the formability of the stainless sheet metals is a major concern for engineers in sheet industries. In the case of austenitic steels, existence of strain-induced martensite ([Formula: see text] -martensite) in their micro structure causes considerable hardening and formab...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947116/ https://www.ncbi.nlm.nih.gov/pubmed/36813804 http://dx.doi.org/10.1038/s41598-023-28719-5 |
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author | Zhang, Mingxiang Meng, Zheng Shariati, Morteza |
author_facet | Zhang, Mingxiang Meng, Zheng Shariati, Morteza |
author_sort | Zhang, Mingxiang |
collection | PubMed |
description | Effect of microstructure on the formability of the stainless sheet metals is a major concern for engineers in sheet industries. In the case of austenitic steels, existence of strain-induced martensite ([Formula: see text] -martensite) in their micro structure causes considerable hardening and formability reduction. In the present study, we aim to evaluate the formability of AISI 316 steels with different intensities of martensite via experimental and artificial intelligence methods. In the first step, AISI 316 grade steels with 2 mm initial thicknesses are annealed and cold rolled to various thicknesses. Subsequently, the relative area of strain-induced martensite are measured using metallography tests. Formability of the rolled sheets are determined using hemisphere punch test to obtain forming limit diagrams (FLDs). The data obtained from experiments were further utilized to train and validate an artificial neural fuzzy interfere system (ANFIS). After training the ANFIS, predicted major strains by the neural network are compared to a new set experimental results. The results indicate that cold rolling has unfavorable effects on the formability of this type of stainless steels while significantly strengthens the sheets. Moreover, the ANFIS exhibits satisfactory results in comparison to the experimental measurements. |
format | Online Article Text |
id | pubmed-9947116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99471162023-02-24 ANFIS-based forming limit prediction of stainless steel 316 sheet metals Zhang, Mingxiang Meng, Zheng Shariati, Morteza Sci Rep Article Effect of microstructure on the formability of the stainless sheet metals is a major concern for engineers in sheet industries. In the case of austenitic steels, existence of strain-induced martensite ([Formula: see text] -martensite) in their micro structure causes considerable hardening and formability reduction. In the present study, we aim to evaluate the formability of AISI 316 steels with different intensities of martensite via experimental and artificial intelligence methods. In the first step, AISI 316 grade steels with 2 mm initial thicknesses are annealed and cold rolled to various thicknesses. Subsequently, the relative area of strain-induced martensite are measured using metallography tests. Formability of the rolled sheets are determined using hemisphere punch test to obtain forming limit diagrams (FLDs). The data obtained from experiments were further utilized to train and validate an artificial neural fuzzy interfere system (ANFIS). After training the ANFIS, predicted major strains by the neural network are compared to a new set experimental results. The results indicate that cold rolling has unfavorable effects on the formability of this type of stainless steels while significantly strengthens the sheets. Moreover, the ANFIS exhibits satisfactory results in comparison to the experimental measurements. Nature Publishing Group UK 2023-02-22 /pmc/articles/PMC9947116/ /pubmed/36813804 http://dx.doi.org/10.1038/s41598-023-28719-5 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 Zhang, Mingxiang Meng, Zheng Shariati, Morteza ANFIS-based forming limit prediction of stainless steel 316 sheet metals |
title | ANFIS-based forming limit prediction of stainless steel 316 sheet metals |
title_full | ANFIS-based forming limit prediction of stainless steel 316 sheet metals |
title_fullStr | ANFIS-based forming limit prediction of stainless steel 316 sheet metals |
title_full_unstemmed | ANFIS-based forming limit prediction of stainless steel 316 sheet metals |
title_short | ANFIS-based forming limit prediction of stainless steel 316 sheet metals |
title_sort | anfis-based forming limit prediction of stainless steel 316 sheet metals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947116/ https://www.ncbi.nlm.nih.gov/pubmed/36813804 http://dx.doi.org/10.1038/s41598-023-28719-5 |
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