| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
Joint Sessions of AIM, ICME, & 3DMS
|
| Presentation Title |
Harnessing Deep Learning Conditional Diffusion Models for Microscopy Modality Transfer of Light Optical Microscopy to Electron Backscattering Microscopy Diffraction Misorientations |
| Author(s) |
Nicholas Amano, Bo Lei, Elizabeth Holm, Dominik Britz, Martin Müller |
| On-Site Speaker (Planned) |
Nicholas Amano |
| Abstract Scope |
Analyzing microstructures is essential in metallurgical science and manufacturing, prompting significant investment in the preparation and imaging of structured materials. In this research, we present a deep learning method that employs conditional diffusion models to generate the electron backscattered diffraction microscopy (EBSD) misorientation maps of quenched and tempered steel images from light optical microscopy (LOM) data. By leveraging the cost effective and relatively easy to produce LOM micrographs to generate high quality EBSD misorientation maps, we hope to accelerate the characterization step during steel manufacturing. This work is supported by a unique dataset of synchronized LOM and EBSD misorientation micrographs taken from the same sample locations and scales. We showcase diffusion models applicability to materials science imaging by reproducing EBSD misorientations from LOM images of highly complex multiphase steel. Our results indicate that diffusion models produce plausible and internally consistent EBSD misorientation mappings, but their absolute values are somewhat unreliable. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |