Prediction of sub-critical fatigue crack growth rate in a high-carbon tempered martensitic steel at varying R ratio: Experimental investigation and machine learning based modelling
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals · Microstructure and Mechanical Properties of Steels · Fatigue and fracture mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (32)
Show source-provided references
1. Anderson TL. Fracture mechanics: fundamentals and applications, fourth edition, fracture mechanics: fundamentals and applications. 4th ed.; 2017.p. 1–661. doi: 10.1201/9781315370293/FRACTURE-MECHANICS-TED-ANDERSON.10.1201/9781315370293 ↗
2. Fatigue of materials
3. Machine learning for materials scientists: an introductory guide toward best practices10.1021/acs.chemmater.0c01907 ↗
4. J. Schmidt, M.R.G. Marques, S. Botti, M.A.L. Marques, Recent advances and applications of machine learning in solid-state materials science, Npj Computational Materials 2019 5:1 5 (2019) 1–36. doi: 10.1038/s41524-019-0221-0.10.1038/s41524-019-0221-0 ↗
5. Machine learning in materials science10.1002/inf2.12028 ↗
6. Deep learning interprets failure process of coal reservoir during CO2-desorption by 3D reconstruction techniques10.1016/j.energy.2023.128802 ↗
7. Deep learning-based pore network generation: Numerical insights into pore geometry effects on microstructural fluid flow behaviors of unconventional resources10.1016/j.energy.2024.130990 ↗
8. Digital Microscopic Multiphase Heterogeneity Representation and Its Effects on Micromechanics and Cracking Behaviors of Geomaterials
9. H. Wang, W. Zhang, F. Sun, W. Zhang, A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation, Materials 2017, Vol. 10, Page 543 10 (2017) 543. doi: 10.3390/MA10050543.10.3390/ma10050543 ↗
10. An artificial neural network modeling approach for short and long fatigue crack propagation10.1016/j.commatsci.2020.109962 ↗
11. D. Jha, L. Ward, A. Paul, W. keng Liao, A. Choudhary, C. Wolverton, A. Agrawal, ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition, Scientific Reports 2018 8:1 8 (2018) 1–13. doi: 10.1038/s41598-018-35934-y.10.1038/s41598-018-35934-y ↗
12. K. Choudhary, B. DeCost, C. Chen, A. Jain, F. Tavazza, R. Cohn, C.W. Park, A. Choudhary, A. Agrawal, S.J.L. Billinge, E. Holm, S.P. Ong, C. Wolverton, Recent advances and applications of deep learning methods in materials science, Npj Computational Materials 2022 8:1 8 (2022) 1–26. doi: 10.1038/s41524-022-00734-6.10.1038/s41524-022-00734-6 ↗
13. White etching structures in annealed 52100 bearing steel arising from high-pressure torsion tests10.1016/j.triboint.2021.107187 ↗
14. Rolling contact fatigue in martensitic 100Cr6: Subsurface hardening and crack formation10.1016/j.msea.2014.03.143 ↗
15. Analysis of white layers formed in hard turning of AISI 52100 steel10.1016/j.msea.2004.08.052 ↗
16. CARBIDE REFINING HEAT TREATMENTS FOR 52100 BEARING STEEL10.1007/BF02643140 ↗
17. K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Machine learning for molecular and materials science, Nature 2018 559:7715 559 (2018) 547–555. doi: 10.1038/s41586-018-0337-2.10.1038/s41586-018-0337-2 ↗
18. Y. Bengio, Practical Recommendations for Gradient-Based Training of Deep Architectures, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7700 LECTURE NO (2012) 437–478. doi: 10.1007/978-3-642-35289-8_26.10.1007/978-3-642-35289-8_26 ↗
19. Service load fatigue damage — a historical perspective10.1016/S0142-1123(99)00054-7 ↗
20. Microstructural effects on the sub-critical fatigue crack growth in nano-bainite10.1016/j.msea.2018.11.114 ↗
21. Improvement of sub-critical fatigue crack growth life by nano-structuring of pearlite10.1016/j.ijfatigue.2019.01.005 ↗
22. Fatigue crack growth in the aluminium alloy D16 under constant and variable amplitude loading10.1016/S0142-1123(03)00067-7 ↗
23. Effect of load ratio and maximum stress intensity on the fatigue threshold in Ti–6Al–4V10.1016/S0013-7944(00)00099-0 ↗
24. A two parameter driving force for fatigue crack growth analysis10.1016/j.ijfatigue.2005.07.002 ↗
25. Crack closure and growth behavior of physically short fatigue cracks under random loading10.1016/S0013-7944(99)00133-2 ↗
26. D.P. Kingma, J.L. Ba, Adam: A Method for Stochastic Optimization, 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (2014). https://arxiv.org/abs/1412.6980v9 (accessed May 13, 2024).
27. An effective algorithm for hyperparameter optimization of neural networks10.1147/JRD.2017.2709578 ↗
28. The Problem of Overfitting10.1021/ci0342472 ↗
29. R. Heckel, F.F. Yilmaz, Early Stopping in Deep Networks: Double Descent and How to Eliminate it, ICLR 2021 - 9th International Conference on Learning Representations (2020). https://arxiv.org/abs/2007.10099v2 (accessed May 13, 2024).
30. Chang · Dropout Feature Ranking for Deep Learning Models · 2017
31. Analysis of Spherical Carbides Formed in Chromium Added Hypereutectoid Bearing Steels10.4028/www.scientific.net/MSF.539-543.4866 ↗
32. Zehnder · Fract. Mech. · 2012 · 6210.1007/978-94-007-2595-9 ↗