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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Auteurs principaux: Ali Riza Durmaz, Martin Müller, Bo Lei, Akhil Thomas, Dominik Britz, Elizabeth A. Holm, Chris Eberl, Frank Mücklich, Peter Gumbsch
Format: article
Langue:EN
Publié: Nature Portfolio 2021
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Accès en ligne:https://doaj.org/article/63e5e4b34b434b15a778aed27ab99f3f
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Résumé: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.