3D variational autoencoder for fingerprinting microstructure volume elements

Microstructure quantification is an important step towards establishing structure–property relationships in materials. Machine learning-based image processing methods have been shown to outperform conventional image processing techniques and are increasingly applied to microstructure quantification...

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Veröffentlicht in:	Computational materials science Jg. 259; S. 114145
Hauptverfasser:	White, Michael D., Atkinson, Michael D., Plowman, Adam J., Shanthraj, Pratheek
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2025
Schlagworte:	Crystal plasticity Feature learning Fingerprinting Machine learning Microstructure characterisation Surrogate model Microstructure characterisation Feature learning Crystal plasticity Fingerprinting Surrogate model Machine learning
ISSN:	0927-0256
Online-Zugang:	Volltext
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