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About this Abstract
Meeting	MS&T22: Materials Science & Technology
Symposium	AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title	Estimation of Sub-micron Carbide Sizes and Morphologies in Dual-Phase Steels from Light Optical Micrographs Using Generative Adversarial Networks
Author(s)	Bo Lei, Martin Müller, Dominik Britz, Frank Mücklich, Elizabeth Holm
On-Site Speaker (Planned)	Bo Lei
Abstract Scope	Mechanical properties of dual-phase steels are highly related to the sizes and morphologies of carbide precipitates. Quantitative measurements rely on image processing of high-resolution SEM micrographs. However, due to the time and cost limitations of SEM imaging, it cannot be used on a large scale. LOM provides fast imaging, but it cannot capture sub-micron characteristics of the carbide precipitates, hence impractical for measurements. Here, we developed an LOM-to-SEM transformation strategy using deep learning and a correlative dataset. We demonstrate that Generative Adversarial Networks (GAN) can be applied to generate high-quality correlative SEM images from LOM images. The approach is further validated by comparing the statistics of the carbide characteristics derived from the synthetic images against real images. The LOM-to-SEM image generation scheme provides a novel route for high-resolution microstructure characterization based only on LOM micrographs.

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