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Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires

The electro-mechanical and electro-thermal properties of high-performance Restacked-Rod-Process (RRP) Nb(3)Sn wires are key factors in the realization of compact magnets above 15 T for the future particle physics experiments. Combining X-ray micro-tomography with unsupervised machine learning algori...

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Detalles Bibliográficos
Autores principales: Bagni, T., Bovone, G., Rack, A., Mauro, D., Barth, C., Matera, D., Buta, F., Senatore, C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032674/
https://www.ncbi.nlm.nih.gov/pubmed/33833396
http://dx.doi.org/10.1038/s41598-021-87475-6
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author Bagni, T.
Bovone, G.
Rack, A.
Mauro, D.
Barth, C.
Matera, D.
Buta, F.
Senatore, C.
author_facet Bagni, T.
Bovone, G.
Rack, A.
Mauro, D.
Barth, C.
Matera, D.
Buta, F.
Senatore, C.
author_sort Bagni, T.
collection PubMed
description The electro-mechanical and electro-thermal properties of high-performance Restacked-Rod-Process (RRP) Nb(3)Sn wires are key factors in the realization of compact magnets above 15 T for the future particle physics experiments. Combining X-ray micro-tomography with unsupervised machine learning algorithm, we provide a new tool capable to study the internal features of RRP wires and unlock different approaches to enhance their performances. Such tool is ideal to characterize the distribution and morphology of the voids that are generated during the heat treatment necessary to form the Nb(3)Sn superconducting phase. Two different types of voids can be detected in this type of wires: one inside the copper matrix and the other inside the Nb(3)Sn sub-elements. The former type can be related to Sn leaking from sub-elements to the copper matrix which leads to poor electro-thermal stability of the whole wire. The second type is detrimental for the electro-mechanical performance of the wires as superconducting wires experience large electromagnetic stresses in high field and high current conditions. We analyze these aspects thoroughly and discuss the potential of the X-ray tomography analysis tool to help modeling and predicting electro-mechanical and electro-thermal behavior of RRP wires and optimize their design.
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spelling pubmed-80326742021-04-09 Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires Bagni, T. Bovone, G. Rack, A. Mauro, D. Barth, C. Matera, D. Buta, F. Senatore, C. Sci Rep Article The electro-mechanical and electro-thermal properties of high-performance Restacked-Rod-Process (RRP) Nb(3)Sn wires are key factors in the realization of compact magnets above 15 T for the future particle physics experiments. Combining X-ray micro-tomography with unsupervised machine learning algorithm, we provide a new tool capable to study the internal features of RRP wires and unlock different approaches to enhance their performances. Such tool is ideal to characterize the distribution and morphology of the voids that are generated during the heat treatment necessary to form the Nb(3)Sn superconducting phase. Two different types of voids can be detected in this type of wires: one inside the copper matrix and the other inside the Nb(3)Sn sub-elements. The former type can be related to Sn leaking from sub-elements to the copper matrix which leads to poor electro-thermal stability of the whole wire. The second type is detrimental for the electro-mechanical performance of the wires as superconducting wires experience large electromagnetic stresses in high field and high current conditions. We analyze these aspects thoroughly and discuss the potential of the X-ray tomography analysis tool to help modeling and predicting electro-mechanical and electro-thermal behavior of RRP wires and optimize their design. Nature Publishing Group UK 2021-04-08 /pmc/articles/PMC8032674/ /pubmed/33833396 http://dx.doi.org/10.1038/s41598-021-87475-6 Text en © The Author(s) 2021 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
Bagni, T.
Bovone, G.
Rack, A.
Mauro, D.
Barth, C.
Matera, D.
Buta, F.
Senatore, C.
Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires
title Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires
title_full Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires
title_fullStr Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires
title_full_unstemmed Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires
title_short Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires
title_sort machine learning applied to x-ray tomography as a new tool to analyze the voids in rrp nb(3)sn wires
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032674/
https://www.ncbi.nlm.nih.gov/pubmed/33833396
http://dx.doi.org/10.1038/s41598-021-87475-6
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