TY - JOUR

T1 - Generative 3D reconstruction of Ti-6Al-4V basketweave microstructures by optimization of differentiable microstructural descriptors

AU - Blümer, Vincent

AU - Safi, Ali Reza

AU - Soyarslan, Celal

AU - Klusemann, Benjamin

AU - van den Boogaard, Ton

N1 - Publisher Copyright: © 2025 The Authors

PY - 2025/6/1

Y1 - 2025/6/1

N2 - 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.

AB - 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.

KW - Convolutional Neural Network

KW - Gram matrices

KW - Microstructure characterization and reconstruction

KW - Multiscale

KW - Titanium

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=105001575211&partnerID=8YFLogxK

U2 - 10.1016/j.actamat.2025.120947

DO - 10.1016/j.actamat.2025.120947

M3 - Journal articles

AN - SCOPUS:105001575211

VL - 291

JO - Acta Materialia

JF - Acta Materialia

SN - 1359-6454

M1 - 120947

ER -