A deep learning approach for complex microstructure inference.

ABSTRACT: Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning's seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30-50 mic

SUBMITTER: Durmaz AR 

PROVIDER: S-EPMC8560760 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature