Machine learning based approach to predict ductile damage model parameters for polycrystalline metals-Reference-Cited by-同舟云学术

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Machine learning based approach to predict ductile damage model parameters for polycrystalline metals

Published:2023-10 Issue: Volume:229 Page:112382
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Blaschke D.N.^ORCID,Nguyen T.,Nitol M.,O’Malley D.,Fensin S.

Funder

U.S. Department of Energy

Publisher

Elsevier BV

Subject

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

Reference39 articles.

1. Rate-dependent ductile failure model;Addessio;J. Appl. Phys.,1993

2. Multiscale modeling of plasticity based on embedding the viscoplastic self-consistent formulation in implicit finite elements;Segurado;Int. J. Plast.,2012

3. Adaptations of Gurson-based ductile damage models for spallation under extreme dynamic loading;Nguyen,2023

4. Void coalescence model for ductile damage;Tonks;AIP Conf. Proc.,2002

5. The Tonks Ductile Damage Model;Tonks,2003

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of the energetics of stable self-interstitial atoms at tungsten grain boundaries via machine learning;Journal of Nuclear Materials;2024-05

2. Multiscale Richtmyer-Meshkov instability experiments to isolate the strain rate dependence of strength;Physical Review E;2024-01-26

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