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

DOI: 10.1038/s41598-021-87475-6
Published: 2021 Apr 8
Container: Scientific reports
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-021-87475-6,
  title = {Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires.},
  author = {Bagni T and Bovone G and Rack A and Mauro D and Barth C and Matera D and Buta F and Senatore C},
  year = {2021},
  journal = {Scientific reports},
  doi = {10.1038/s41598-021-87475-6},
  url = {https://doi.org/10.1038/s41598-021-87475-6}
}

RIS

TY  - JOUR
TI  - Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires.
AU  - Bagni T
AU  - Bovone G
AU  - Rack A
AU  - Mauro D
AU  - Barth C
AU  - Matera D
AU  - Buta F
AU  - Senatore C
PY  - 2021
JO  - Scientific reports
DO  - 10.1038/s41598-021-87475-6
UR  - https://doi.org/10.1038/s41598-021-87475-6
ER  -

APA

T, B., G, B., A, R., D, M., C, B., D, M., F, B., & C, S. (2021). Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb(3)Sn wires.. Scientific reports. https://doi.org/10.1038/s41598-021-87475-6

Source records

pubmed · retrieved 2026-11-10T11:41:21.159Z