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The microstructure of material strongly influences its mechanical properties and the microstructure itself is influenced by the processing conditions. Thus, establishing a Process-Structure-Property relationship is a crucial task in material design and is of interest in many engineering applications. We develop a GAN (Generative Adversarial Network) to synthesize microstructures based on given processing conditions. This approach is devoid of feature engineering, needs little domain awareness, and can be applied to a wide variety of material systems. Results show that our GAN model can produce high-fidelity multiphase microstructures that have a good correlation with the given processing conditions.

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Establishing Process-Structure Linkages Using Generative Adversarial Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Mohammad Safiuddin,
  • CH. Likith Reddy,
  • Ganesh Vasantada,
  • C. H. J. N. S. Harsha,
  • S. Gangolu

摘要

The microstructure of material strongly influences its mechanical properties and the microstructure itself is influenced by the processing conditions. Thus, establishing a Process-Structure-Property relationship is a crucial task in material design and is of interest in many engineering applications. We develop a GAN (Generative Adversarial Network) to synthesize microstructures based on given processing conditions. This approach is devoid of feature engineering, needs little domain awareness, and can be applied to a wide variety of material systems. Results show that our GAN model can produce high-fidelity multiphase microstructures that have a good correlation with the given processing conditions.