A physics-informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth
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OpenAlex topics: Non-Destructive Testing Techniques · Nuclear Engineering Thermal-Hydraulics · Model Reduction and Neural Networks
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References (51)
Show source-provided references
1. Metallurgical failure analysis of GP905 reboiler, technical information, longford royal commission
2. Recent developments in brittle and quasi-brittle failure assessment of engineering materials by means of local approaches10.1016/j.mser.2013.11.001 ↗
3. Fatigue crack growth in metals and alloys: mechanisms and micromechanics10.1179/imr.1992.37.1.45 ↗
4. Recent developments in fatigue crack growth assessment10.1016/j.ijfatigue.2006.01.004 ↗
5. A review of fatigue crack growth resistance in the short crack growth regime10.1016/j.msea.2019.03.102 ↗
6. Recent research progress on mechanisms and models of fatigue crack growth for metallic materials
7. Analysis of stresses and strains near the end of a crack traversing a plate10.1115/1.4011547 ↗
8. A path independent integral and the approximate analysis of strain concentration by notches and cracks10.1115/1.3601206 ↗
9. Fatigue crack growth and damage tolerance10.1111/ffe.12155 ↗
10. A critical analysis of crack propagation laws10.1115/1.3656900 ↗
11. Numerical analysis of crack propagation in cyclic-loaded structures10.1115/1.3609637 ↗
12. The prediction of fatigue life in the crack initiation and propagation stages—a state of the art survey10.1016/0013-7944(79)90015-8 ↗
13. A cumulative model of fatigue crack growth10.1016/0142-1123(82)90061-5 ↗
14. Comparison of several fatigue crack propagation laws with experimental results10.1016/0013-7944(74)90031-9 ↗
15. A fatigue crack propagation model10.1016/0013-7944(84)90079-1 ↗
16. A new method to predict fatigue crack growth rate of materials based on average cyclic plasticity strain damage accumulation10.1016/j.cja.2012.12.013 ↗
17. Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty10.1016/j.ress.2015.10.002 ↗
18. Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit10.1016/j.ress.2023.109753 ↗
19. Digital twin-driven intelligent assessment of gear surface degradation10.1016/j.ymssp.2022.109896 ↗
20. Prediction of fatigue–crack growth with neural network-based increment learning scheme10.1016/j.engfracmech.2020.107402 ↗
21. An artificial neural network-based algorithm for evaluation of fatigue crack propagation considering nonlinear damage accumulation10.3390/ma9060483 ↗
22. Fatigue behavior prediction and analysis of shot peened mild carbon steels10.1016/j.ijfatigue.2018.06.004 ↗
23. The use of neural networks for the prediction of fatigue lives of composite materials10.1016/S1359-835X(99)00027-5 ↗
24. Prediction of high cycle fatigue property of Ti-6Al-4V alloy using artificial neural network10.4028/www.scientific.net/MSF.849.360 ↗
25. Machine learning-based real-time visible fatigue crack growth detection10.1016/j.dcan.2021.03.003 ↗
26. A deep learning-based fatigue crack growth rate measurement method using mobile phones10.1016/j.ijfatigue.2022.107327 ↗
27. Multi-feature vision transformer for automatic defect detection and quantification in composites using thermography10.1016/j.ndteint.2023.103033 ↗
28. Deep learning-based planar crack damage evaluation using convolutional neural networks10.1016/j.engfracmech.2021.107604 ↗
29. An intelligent crack damage assessment method by integrating information and physics10.1016/j.engfracmech.2023.109737 ↗
30. Fast evaluation of crack growth path using time series forecasting10.1016/j.engfracmech.2019.106567 ↗
31. Application of tabular data synthesis using generative adversarial networks on machine learning-based multiaxial fatigue life prediction10.1016/j.ijpvp.2022.104779 ↗
32. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations10.1016/j.jcp.2018.10.045 ↗
33. Scientific machine learning through physics–informed neural networks: Where we are and what’s next10.1007/s10915-022-01939-z ↗
34. Using hybrid physics-informed neural networks to predict lifetime under multiaxial fatigue loading10.1016/j.engfracmech.2023.109351 ↗
35. Physics-informed neural networks for corrosion-fatigue prognosis
36. Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning
37. Physics-informed residual network (PIResNet) for rolling element bearing fault diagnostics10.1016/j.ymssp.2023.110544 ↗
38. Physics-guided machine learning frameworks for fatigue life prediction of AM materials10.1016/j.ijfatigue.2023.107658 ↗
39. Using physics-informed neural networks with small datasets to predict the length of gas turbine nozzle cracks10.1016/j.aei.2023.102232 ↗
40. A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing10.1016/j.matdes.2022.111089 ↗
41. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network10.1016/j.ijfatigue.2022.107234 ↗
42. A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials10.1016/j.cma.2022.114587 ↗
43. Neural networks fail to learn periodic functions and how to fix it
44. A systematic review on overfitting control in shallow and deep neural networks10.1007/s10462-021-09975-1 ↗
45. Adaptive activation functions accelerate convergence in deep and physics-informed neural networks10.1016/j.jcp.2019.109136 ↗
46. Stochastic gradient learning in neural networks
47. D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, (2014).
48. Automatic differentiation in machine learning: a survey
49. The statistical nature of fatigue crack propagation10.1115/1.3443666 ↗
50. A study of stochastic fatigue crack growth modeling through experimental data10.1016/S0266-8920(02)00053-X ↗
51. Batch normalization: Accelerating deep network training by reducing internal covariate shift