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Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718

Published online by Cambridge University Press:  30 July 2021

Stephen Taller ,
Luke Scime  and
Kurt Terrani
Stephen Taller
Affiliation:
Oak Ridge National Laboratory, United States
Luke Scime
Affiliation:
Oak Ridge National Laboratory, United States
Kurt Terrani
Affiliation:
Oak Ridge National Laboratory, United States

Abstract

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Type
Advanced Characterization of Components Fabricated by Additive Manufacturing
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 3110 - 3112
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Mali, H.S., Unune, D.R., Machinability of Nickel-Based Superalloys: An Overview, Elsevier Ltd., 2017. https://doi.org/10.1016/b978-0-12-803581-8.09817-9.Google Scholar
Scime, L., Siddel, D., Baird, S., Paquit, V., Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation, Addit. Manuf. 36 (2020) 101453. https://doi.org/10.1016/j.addma.2020.101453.Google Scholar
Cliff, G., Lorimer, G.W., The quantitative analysis of thin specimens, J. Microsc. 103 (1975) 203–207. https://doi.org/10.1111/j.1365-2818.1975.tb03895.x.CrossRefGoogle Scholar
This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).Google Scholar