Quantitative prediction of fracture toughness (<i>K</i><sub>I<i>c</i></sub>) of polymer by fractography using deep neural networks-Reference-Cited by-同舟云学术

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Quantitative prediction of fracture toughness (K_Ic) of polymer by fractography using deep neural networks

Published:2022-09-05 Issue:1 Volume:2 Page:310-321
ISSN:2766-0400
Container-title:Science and Technology of Advanced Materials: Methods
language:en
Short-container-title:Science and Technology of Advanced Materials: Methods

Author:

Mototake Y.¹^ORCID,Ito K.²^ORCID,Demura M.²^ORCID

Affiliation:

1. Research Center for Statistical Machine Learning, The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan

2. Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Japan

Funder

Council for Science, Technology and Innovation

JSPS

Publisher

Informa UK Limited

Subject

General Medicine

Link

https://www.tandfonline.com/doi/pdf/10.1080/27660400.2022.2107883

Reference30 articles.

1. Fatigue of Materials

2. Fractal character of fracture surfaces of metals

3. Correlating fracture toughness and fracture surface roughness via correlation length scale

4. Ervin EU. Quantitative fractography. Boston (MA): Springer US; 1986. pp. 101–122.

5. Fractographic classification in metallic materials by using computer vision

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. Using deep learning method to predict dimensionless values of stress intensity factors and T‐stress of edge notch disk bend (ENDB) specimen;Fatigue & Fracture of Engineering Materials & Structures;2024-05-13

2. Predicting fatigue crack growth metrics from fractographs: Towards fractography by computer vision;International Journal of Fatigue;2023-12

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