journal article·2023-12

Predicting fatigue crack growth metrics from fractographs: Towards fractography by computer vision

Katelyn Jones, William D. Musinski, Adam L. Pilchak, Reji John, Paul A. Shade, Anthony D. Rollett, Elizabeth A. Holm

Publisher page

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: Non-Destructive Testing Techniques · Mineral Processing and Grinding · Fatigue and fracture mechanics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (28)

Show source-provided references
1. High-cycle fatigue of Ti–6Al–4V10.1046/j.1460-2695.1999.00194.x ↗
2. Toward quantitative fractography using convolutional neural networks10.1016/j.engfracmech.2020.106992 ↗
3. Deep learning10.1038/nature14539 ↗
4. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects10.1016/j.apsusc.2013.09.002 ↗
5. Deep materials informatics: Applications of deep learning in materials science10.1557/mrc.2019.73 ↗
6. DeCost · 2016
7. Microstructure cluster analysis with transfer learning and unsupervised learning10.1007/s40192-018-0116-9 ↗
8. Zhuang · 2020
9. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
10. A computer vision based machine learning approach for fatigue crack initiation sites recognition10.1016/j.commatsci.2019.109259 ↗
11. Design of an interpretable Convolutional Neural Network for stress concentration prediction in rough surfaces10.1016/j.matchar.2019.109961 ↗
12. Transfer learning-based algorithms for the detection of fatigue crack initiation sites: A comparative study
13. Deep learning for fractographic classification in metallic materials10.1016/j.engfailanal.2020.104532 ↗
14. Unsupervised machine learning in fractography: Evaluation and interpretation10.1016/j.matchar.2021.111551 ↗
15. Quantitative prediction of fracture toughness (KIc) of polymer by fractography using deep neural networks
16. Gallager · 2001
17. Simonyan · 2014
18. Combining fractography with high-energy x-ray diffraction to study fatigue crack growth in Ti-6Al-4V10.1007/s11837-019-03826-9 ↗
19. 2015
20. Abadi · 2015
21. Chollet F et al. Keras. 2015, [cited 2022-09-13], URL https://keras.io.
22. scikit-image: image processing in python10.7717/peerj.453 ↗
23. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
24. Hinton · 2012
25. Cai · 2019
26. Selvaraju · 2016
27. Takahashi · 2018
28. Effect of load ratio and maximum stress intensity on the fatigue threshold in Ti–6Al–4V10.1016/S0013-7944(00)00099-0 ↗