Predicting grain boundary damage by machine learning-Reference-Cited by-同舟云学术

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Predicting grain boundary damage by machine learning

Published:2022-03 Issue: Volume:150 Page:103186
ISSN:0749-6419
Container-title:International Journal of Plasticity
language:en
Short-container-title:International Journal of Plasticity

Author:

Zhang Sheng,Wang Leyun,Zhu Gaoming,Diehl Martin,Maldar Alireza,Shang Xiaoqing,Zeng Xiaoqin

Funder

Shanghai Rising-Star Program

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference49 articles.

1. Deep learning for plasticity and thermo-viscoplasticity;Abueidda;Int. J. Plast.,2021

2. The role of heterogeneous deformation on damage nucleation at grain boundaries in single phase metals;Bieler;Int. J. Plast.,2009

3. Grain boundaries and interfaces in slip transfer;Bieler;Curr. Opin. Solid State Mater. Sci.,2014

4. Stress fields and geometrically necessary dislocation density distributions near the head of a blocked slip band;Britton;Acta Mater,2012

5. XGBoost: a scalable tree boosting system;Chen,2016

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