Hourly Work of 3D Microstructural Visualization of Dual Phase Steels by SliceGAN

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Virtual microstructure design for steels using generative adversarial networks

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Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion

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Summary: SliceGAN is a generative approach that constructs complex 3D images from 2D image examples, making it particularly useful for studying microstructured materials. It leverages high-quality 2D imaging techniques to create 3D datasets, eliminating the need for challenging 3D training data. This approach demonstrates promising potential for design optimization, with the ability to generate high-fidelity 3D datasets using a single representative 2D image.

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Stress-strain partitioning behavior and mechanical properties of dual-phase steel using finite element analysis

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Hourly Work of 3D Microstructural Visualization of Dual Phase Steels by SliceGAN

Related references

Generative Adversarial Networks-Based Synthetic Microstructures for Data-Driven Materials Design

Virtual microstructure design for steels using generative adversarial networks

Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion

Stress-strain partitioning behavior and mechanical properties of dual-phase steel using finite element analysis

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