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February 23, 2026Open Access

Implementation of deep learning-based segmentation for quantitative microstructure analysis and 3D reconstruction in aluminum alloys

Angelos Theofilatos Laszlo Sturz Alexandre Viardin

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Key Points

The aim is to explore deep learning techniques for analyzing the microstructure of aluminum alloys through segmentation and 3D reconstruction.
Applied deep learning algorithms for image segmentation.
Conducted quantitative microstructure analysis on aluminum alloys.
Performed 3D reconstruction based on segmented data.
Achieved improved accuracy in microstructure segmentation.
Facilitated detailed 3D reconstructions of aluminum alloys.
Enabled enhanced quantitative analysis compared to traditional methods.

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Implication

Deep learning implementation improves microstructure analysis in aluminum alloys, suggesting enhanced quantitative assessment.

Abstract

Figures (CT, LOM) produced by Golo Zimmermann, RWTH Aachen University, Foundry Institute, Germany

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Cite This Study

Theofilatos et al. (2026) studied this question.

synapsesocial.com/papers/69a1359eed1d949a99abfb3e https://doi.org/10.5281/zenodo.18740450

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