Failure classification of porous additively manufactured parts using Deep Learning-Reference-Cited by-同舟云学术

Failure classification of porous additively manufactured parts using Deep Learning

Published:2022-03 Issue: Volume:204 Page:111098
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Johnson Kyle L.^ORCID,Maestas Demitri,Emery John M.^ORCID,Grigoriu Mircea D.,Smith Matthew D.,Martinez Carianne

Funder

National Nuclear Security Administration

Sandia National Laboratories

U.S. Department of Energy

Laboratory Directed Research and Development

Publisher

Elsevier BV

Subject

Computational Mathematics,General Physics and Astronomy,Mechanics of Materials,General Materials Science,General Chemistry,General Computer Science

Reference29 articles.

1. Automated high-throughput tensile testing reveals stochastic process parameter sensitivity;Heckman;Mater. Sci. Eng. A,2020

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3. Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel;Madison;AIP Conf. Proc.,2018

4. Predicting the reliability of an additively-manufactured metal part for the third sandia fracture challenge by accounting for random material defects;Johnson;Int. J. Fract.,2019

5. A void descriptor function to uniquely characterize pore networks and predict ductile-metal failure properties;Erickson;Int. J. Fract.,2020

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