An image recognition based multiaxial low-cycle fatigue life prediction method with CNN model
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: Fatigue and fracture mechanics · Non-Destructive Testing Techniques · Hydrogen embrittlement and corrosion behaviors in metals
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (43)
Show source-provided references
1. Fatigue of engineering structures under combined nonproportional loads: An overview10.1111/ffe.12834 ↗
2. Soft computing methods for fatigue life estimation: A review of the current state and future trends10.1111/ffe.13343 ↗
3. Fatigue modeling using neural networks: A comprehensive review
4. A Stress-Strain Function for the Fatigue of Metals
5. A critical plane-strain energy density criterion for multiaxial low-cycle fatigue life under non-proportional loading
6. A new energy-critical plane parameter for fatigue life assessment of various metallic materials subjected to in-phase and out-of-phase multiaxial fatigue loading conditions10.1016/S0142-1123(00)00002-5 ↗
7. A modified strain energy density exhaustion model for creep–fatigue life prediction10.1016/j.ijfatigue.2016.03.005 ↗
8. Modelling multiaxial fatigue with a new combination of critical plane definition and energy-based criterion10.1016/j.ijfatigue.2017.12.004 ↗
9. Equivalent energy-based critical plane fatigue damage parameter for multiaxial LCF under variable amplitude loading10.1016/j.ijfatigue.2019.105350 ↗
10. Life prediction for rate-dependent low-cycle fatigue of PA6 polymer considering ratchetting: Semi-empirical model and neural network based approach10.1016/j.ijfatigue.2020.105619 ↗
11. A hybrid ANN-multiaxial fatigue nonlocal model to estimate fretting fatigue life for aeronautical Al alloys10.1016/j.ijfatigue.2022.107011 ↗
12. A novel method of multiaxial fatigue life prediction based on deep learning10.1016/j.ijfatigue.2021.106356 ↗
13. Using machine learning to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading10.1016/j.ijfatigue.2022.107067 ↗
14. A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction10.1016/j.ijfatigue.2022.106996 ↗
15. A fatigue life prediction approach for laser-directed energy deposition titanium alloys by using support vector regression based on pore-induced failures10.1016/j.ijfatigue.2022.106748 ↗
16. Fatigue life prediction considering mean stress effect based on random forests and kernel extreme learning machine10.1016/j.ijfatigue.2022.106761 ↗
17. Machine learning assisted probabilistic creep-fatigue damage assessment10.1016/j.ijfatigue.2021.106677 ↗
18. Remaining useful life prediction of wind turbine generator based on 1D-CNN and Bi-LSTM10.1016/j.ijfatigue.2022.107051 ↗
19. Prediction of low-cycle fatigue crack development of sputtered Cu thin film using deep convolutional neural network10.1016/j.ijfatigue.2022.106998 ↗
20. Automatic detection of fatigue crack paths using digital image correlation and convolutional neural networks10.1111/ffe.13433 ↗
21. Data augmentation in microscopic images for material data mining10.1038/s41524-020-00392-6 ↗
22. Fast stress evaluation of the top coat of thermal barrier coatings under CaO–MgO–Al2O3–SiO2 penetration based on image recognition and an artificial neural network10.1016/j.ceramint.2021.03.145 ↗
23. Recent advances in black box and white-box models for urban heat island prediction: Implications of fusing the two methods10.1016/j.rser.2022.112520 ↗
24. Machine learning-based genetic feature identification and fatigue life prediction10.1111/ffe.13532 ↗
25. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures10.1016/j.engfracmech.2021.108130 ↗
26. On Interpretability of Artificial Neural Networks: A Survey10.1109/TRPMS.2021.3066428 ↗
27. Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks. 13th European Conference on Computer Vision (ECCV-2014), 2014; Zurich, Switzerland.10.1007/978-3-319-10590-1_53 ↗
28. Wang Z, Yang J. Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2018; New Orleans, LA, USA.
29. Sundararajan M, Taly A, Yan Q. Axiomatic Attribution for Deep Networks. Thirty-fourth International Conference on Machine Learning (ICML-2017), 2017; Sydney, Australia.
30. Cyclic Deformation and Low-Cycle Fatigue for 316LN Stainless Steel under Non-proportional Loading10.1051/matecconf/201930008002 ↗
31. Martensitic transformation of an austenitic stainless steel under non-proportional cyclic loading10.1016/j.ijfatigue.2019.03.020 ↗
32. Nonproportional cyclic plastic behavior of steel 40
33. Cyclic deformation of 316L stainless steel and constitutive modeling under non-proportional variable loading path10.1016/j.ijplas.2019.04.016 ↗
34. Uniaxial ratcheting deformation of 316LN stainless steel with dynamic strain aging: Experiments and simulation10.1016/j.ijsolstr.2020.10.017 ↗
35. A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals10.1016/j.compind.2018.12.013 ↗
36. Automated semantic segmentation of NiCrBSi-WC optical microscopy images using convolutional neural networks10.1016/j.commatsci.2022.111391 ↗
37. Corpus English word detection and image recognition algorithm based on improved convolutional neural network10.1016/j.micpro.2021.103920 ↗
38. Boureau Y-L, Ponce J, LeCun Y. A theoretical analysis of feature pooling in visual recognition. the 27th International Conference on Machine Learning (ICML-10), 2010; Haifa, Israel.
39. Lin M, Chen Q, Yan S. Network in Network. International Conference on Learning Representations (ICLR-2014), 2014; Banff, Canada.
40. Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks
41. Predicting stress–strain curves using transfer learning: Knowledge transfer across polymer composites10.1016/j.matdes.2022.110700 ↗
42. Determination of ductile fracture properties of 16MND5 steels under varying constraint levels using machine learning methods10.1016/j.ijmecsci.2022.107331 ↗
43. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016.10.1109/CVPR.2016.90 ↗