Generative 3D reconstruction of Ti-6Al-4V basketweave microstructures by optimization of differentiable microstructural descriptors

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Authors

We present a methodology for the generative reconstruction of 3D microstructures from 2D cross-sectional electron backscatter diffraction micrographs. The method is applied to Ti-6Al-4V processed by laser powder bed fusion, where a high amount of basketweave morphology is observed, which arises from the solid-state β→α-transition upon cooling. Prior-β-grain reconstruction is performed and the out-of-plane orientation of the observed grains is obtained leveraging Burgers orientation relationship. Microstructural descriptors related to convolutional neural networks are extracted from the 2D micrographs, and used for cross-section-based optimization of pixel values in a 3D volume. In order to reconstruct crystallographic orientations, the orientation distribution of the basketweave microstructure is reduced to a discrete set of characteristic orientations, which are sequentially reconstructed as separate components. Our reconstructions capture the characteristic lath morphology that is typically observed in powder bed fusion-processed Ti-6Al-4V and perform well in comparisons of chord length, as well as grain size, aspect ratio, and axis orientation distributions.

Original languageEnglish
Article number120947
JournalActa Materialia
Volume291
Number of pages10
ISSN1359-6454
DOIs
Publication statusPublished - 01.06.2025

Bibliographical note

Publisher Copyright:
© 2025 The Authors

    Research areas

  • Convolutional Neural Network, Gram matrices, Microstructure characterization and reconstruction, Multiscale, Titanium
  • Engineering

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