Fatigue Crack Growth Rate Prediction in Nickle based Super-alloys using Machine Learning Algorithm-Reference-Cited by-同舟云学术

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Fatigue Crack Growth Rate Prediction in Nickle based Super-alloys using Machine Learning Algorithm

Published:2024 Issue: Volume:60 Page:382-389
ISSN:2452-3216
Container-title:Procedia Structural Integrity
language:en
Short-container-title:Procedia Structural Integrity

Author:

Mahesh S.,Anil Chandra A.R.,Kumar L. Ravi,Manjunatha C.M.

Publisher

Elsevier BV

Reference18 articles.

1. American Society for Testing and Materials, 2008. Standard test method for measurement of fatigue crack growth rates: designation: E 647-08. ASTM international.

2. Anderson, T.L., 2017. Fracture mechanics: fundamentals and applications. CRC press.

3. Broek, D., 2012. Elementary engineering fracture mechanics. Springer Science & Business Media.

4. Brownlee, J., 2016. Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end. Machine Learning Mastery.

5. Effect of load ratio and maximum stress intensity on the fatigue threshold in Ti–6Al–4V;Boyce;Engineering Fracture Mechanics,2001

Cited by 1 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 Tensile Properties from Micro-structure in Metallic Alloys using Machine Learning;2024 International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS);2024-04-22

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