What question did this study set out to answer?

This research aims to develop a method for generating realistic 3D structures from 2D pore images in selective laser melting.

CAGAN3D: A Generative Adversarial Network Framework for Generating 3D Structures from 2D Pore Images in Selective Laser Melting

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Randomized trial demonstrates efficient 3D structure generation from 2D pore images, indicating a novel approach for porosity analysis in SLM.

This research aims to develop a method for generating realistic 3D structures from 2D pore images in selective laser melting.
Developed a generative adversarial network framework (CAGAN3D) with a 3D coordinate attention module.
Compared performance with state-of-the-art algorithms (CEM3DMG, SliceGAN).
Evaluated porosity consistency using the Archimedes drainage method.
CAGAN3D shows a relative error in porosity measurement as low as 0.73%.
The model excelled in capturing spatial distribution and morphological features of pores.
Demonstrated significant advantages over existing methods in reproducing fine pore features.

Tian et al. (2026) studied this question.

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Randomized trial demonstrates efficient 3D structure generation from 2D pore images, indicating a novel approach for porosity analysis in SLM.