Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms-Reference-Cited by-同舟云学术

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Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

Published:2024-06-03 Issue:3 Volume:12 Page:447-464
ISSN:2095-3127
Container-title:Advances in Manufacturing
language:en
Short-container-title:Adv. Manuf.

Author:

Li Si-Geng,Chen Qiu-Ren,Huang Li^ORCID,Chen Min,Wei Chen-Di,Yue Zhong-Jie,Liu Ru-Xue,Tong Chao,Liu Qing

Funder

Key Basic Research Project of Suzhou

Key Technologies Research and Development Program

Innovative Research Group Project of the National Natural Science Foundation of China

Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40436-024-00491-3.pdf

Reference40 articles.

1. Long X, Lu C, Su Y et al (2023) Machine learning framework for predicting the low cycle fatigue life of lead-free solders. Eng Fail Anal 148:107228. https://doi.org/10.1016/j.engfailanal.2023.107228

2. Hao S, Cui L, Jiang D et al (2013) A transforming metal nanocomposite with large elastic strain, low modulus, and high strength. Science 339(6124):1191–1194

3. Sun X, Zhou K, Shi S et al (2022) A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction. Int J Fatigue 162:106996. https://doi.org/10.1016/j.ijfatigue.2022.106996

4. Zhang M, Sun CN, Zhang X et al (2019) High cycle fatigue life prediction of laser additive manufactured stainless steel: a machine learning approach. Int J Fatigue 128:105194. https://doi.org/10.1016/j.ijfatigue.2019.105194

5. Yang WK, Hu BL, Luo YW et al (2023) Understanding geometrical size effect on fatigue life of A588 steel using a machine learning approach. Int J Fatigue 172:107671. https://doi.org/10.1016/j.ijfatigue.2023.107671

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