Austenitic parent grain reconstruction in martensitic steel using deep learning

Fernandez-Zelaia, Patxi; Rossy, Andres Marquez; Campbell, Quinn; Nycz, Andrzej; Ledford, Christopher; Kirka, Michael M.

doi:10.1016/j.matchar.2022.111759

Austenitic parent grain reconstruction in martensitic steel using deep learning

Journal Article · Sat Jan 29 00:00:00 EST 2022 · Materials Characterization

DOI:https://doi.org/10.1016/j.matchar.2022.111759· OSTI ID:1843710

Fernandez-Zelaia, Patxi ^[1]; Rossy, Andres Marquez ^[1]; ^[1]; ^[1]; Ledford, Christopher ^[1]; ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

In this work we develop a deep convolutional architecture to estimate the prior austenite structure from observed martensite electron backscatter diffraction micrographs. A novel data augmentation strategy randomizes the global reference coordinate system which makes it possible to train our model from only four micrographs. The model is much faster than algorithmic approaches and generalizes well when applied to micrographs of a different material. Empirical evidence suggests the efficacy of the model depends on the scale of the microstructure and receptive field of the vision model. Furthermore, this work demonstrates that modern computer vision approaches are well suited for capturing complex spatial-orientation patterns present in orientation imaging micrographs.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1843710

Alternate ID(s):: OSTI ID: 1843635

Journal Information:: Materials Characterization, Journal Name: Materials Characterization Vol. 185; ISSN 1044-5803

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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