Data-driven void growth prediction of aluminum under monotonic tension using deep learning-Reference-Cited by-同舟云学术

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Data-driven void growth prediction of aluminum under monotonic tension using deep learning

Published:2024-11 Issue: Volume:222 Page:109002
ISSN:0143-974X
Container-title:Journal of Constructional Steel Research
language:en
Short-container-title:Journal of Constructional Steel Research

Author:

Wang Xin-Jie,Li Yun-Fan,Gu Tianyu,Xiang Ping,Cheng Sibo,Jia Liang-Jiu

Funder

ANR

Publisher

Elsevier BV

Reference52 articles.

1. The use of aluminum alloys in structures: review and outlook;Sun;Structures,2023

2. Predicting fracture in civil engineering steel structures: state of the art;Kanvinde;J. Struct. Eng.,2017

3. Stress–strain behaviors in large plastic strain ranges under monotonic loading;Jia;Springer Tracts Civil Eng.,2019

4. A criterion for ductile fracture by the growth of holes;McClintock;J. Appl. Mech.,1968

5. On the ductile enlargement of voids in triaxial stress fields∗;Rice;J. Mech. Phys. Solids,1969

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