A deep learning approach for complex microstructure inference

Segmentation and classification of microstructures are required by quality control and materials development. The authors apply deep learning for the segmentation of complex phase steel microstructures, providing a bridge between experimental and computational methods for materials analysis.

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Autores principales:	Ali Riza Durmaz, Martin Müller, Bo Lei, Akhil Thomas, Dominik Britz, Elizabeth A. Holm, Chris Eberl, Frank Mücklich, Peter Gumbsch
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/63e5e4b34b434b15a778aed27ab99f3f
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spelling	oai:doaj.org-article:63e5e4b34b434b15a778aed27ab99f3f2021-11-08T11:07:30ZA deep learning approach for complex microstructure inference10.1038/s41467-021-26565-52041-1723https://doaj.org/article/63e5e4b34b434b15a778aed27ab99f3f2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26565-5https://doaj.org/toc/2041-1723Segmentation and classification of microstructures are required by quality control and materials development. The authors apply deep learning for the segmentation of complex phase steel microstructures, providing a bridge between experimental and computational methods for materials analysis.Ali Riza DurmazMartin MüllerBo LeiAkhil ThomasDominik BritzElizabeth A. HolmChris EberlFrank MücklichPeter GumbschNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ali Riza Durmaz Martin Müller Bo Lei Akhil Thomas Dominik Britz Elizabeth A. Holm Chris Eberl Frank Mücklich Peter Gumbsch A deep learning approach for complex microstructure inference
description	Segmentation and classification of microstructures are required by quality control and materials development. The authors apply deep learning for the segmentation of complex phase steel microstructures, providing a bridge between experimental and computational methods for materials analysis.
format	article
author	Ali Riza Durmaz Martin Müller Bo Lei Akhil Thomas Dominik Britz Elizabeth A. Holm Chris Eberl Frank Mücklich Peter Gumbsch
author_facet	Ali Riza Durmaz Martin Müller Bo Lei Akhil Thomas Dominik Britz Elizabeth A. Holm Chris Eberl Frank Mücklich Peter Gumbsch
author_sort	Ali Riza Durmaz
title	A deep learning approach for complex microstructure inference
title_short	A deep learning approach for complex microstructure inference
title_full	A deep learning approach for complex microstructure inference
title_fullStr	A deep learning approach for complex microstructure inference
title_full_unstemmed	A deep learning approach for complex microstructure inference
title_sort	deep learning approach for complex microstructure inference
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/63e5e4b34b434b15a778aed27ab99f3f
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A deep learning approach for complex microstructure inference

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