The stress-life curve (S-N) and low-cycle strain-life curve (E-N) are the two primary representations used to characterize the fatigue behavior of a material. These material fatigue curves are essential for structural fatigue analysis. However, conducting material fatigue tests is expensive and time-intensive. To address the challenge of data limitations on ferrous metal materials, we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S-N and E-N curves of ferrous materials. In addition, a data-augmentation framework is introduced using a conditional generative adversarial network (cGAN) to overcome data deficiencies. By incorporating the cGAN-generated data, the accuracy (R2) of the Random Forest Algorithm-trained model is improved by 0.3-0.6. It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00491-3
Si-Geng Li
,
Qiu-Ren Chen
,
Li Huang
,
Min Chen
,
Chen-Di Wei
,
Zhong-Jie Yue
,
Ru-Xue Liu
,
Chao Tong
,
Qing Liu
. Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms[J]. Advances in Manufacturing, 2024
, 12(3)
: 447
-464
.
DOI: 10.1007/s40436-024-00491-3
1. Long X, Lu C, Su Y et al (2023) Machine learning framework for predicting the low cycle fatigue life of lead-free solders. Eng Fail Anal 148:107228. https://doi.org/10.1016/j.engfailanal.2023.107228
2. Hao S, Cui L, Jiang D et al (2013) A transforming metal nanocomposite with large elastic strain, low modulus, and high strength. Science 339(6124):1191-1194
3. Sun X, Zhou K, Shi S et al (2022) A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction. Int J Fatigue 162:106996. https://doi.org/10.1016/j.ijfatigue.2022.106996
4. Zhang M, Sun CN, Zhang X et al (2019) High cycle fatigue life prediction of laser additive manufactured stainless steel:a machine learning approach. Int J Fatigue 128:105194. https://doi.org/10.1016/j.ijfatigue.2019.105194
5. Yang WK, Hu BL, Luo YW et al (2023) Understanding geometrical size effect on fatigue life of A588 steel using a machine learning approach. Int J Fatigue 172:107671. https://doi.org/10.1016/j.ijfatigue.2023.107671
6. Wei X, Zhang C, Han S et al (2022) High cycle fatigue S-N curve prediction of steels based on transfer learning guided long short term memory network. Int J Fatigue 163:107050. https://doi.org/10.1016/j.ijfatigue.2022.107050
7. Zhan Z, Li H (2021) Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L. Int J Fatigue 142:105941. https://doi.org/10.1016/j.ijfatigue.2020.105941
8. He L, Wang Z, Ogawa Y et al (2022) Machine-learning-based investigation into the effect of defect/inclusion on fatigue behavior in steels. Int J Fatigue 155:106597. https://doi.org/10.1016/j.ijfatigue.2021.106597
9. Li H, Zhang J, Hu L et al (2023) Notch fatigue life prediction of micro-shot peened 25CRMO4 alloy steel:a comparison between fracture mechanics and machine learning methods. Eng Fract Mech 277:108992. https://doi.org/10.1016/j.engfracmech.2022.108992
10. Zhou T, Sun X, Chen X (2023) A multiaxial low-cycle fatigue prediction method under irregular loading by ANN model with knowledge-based features. Int J Fatigue 176:107868. https://doi.org/10.1016/j.ijfatigue.2023.107868
11. Srinivasan V (2003) Low cycle fatigue and creep-fatigue interaction behavior of 316L (N) stainless steel and life prediction by artificial neural network approach. Int J Fatigue 25(12):1327-1338
12. Michalski RS, Carbonell JG, Mitchell TM (1983) Machine learning an artificial intelligence approach. Springer Berlin, Heidelberg
13. Jones BA, Li W, Nachtsheim CJ et al (2007) Model discrimination-another perspective on model-robust designs. J Stat Plan Infer 137:1577-1583
14. Roy A (n.d.) A novel conditional wasserstein deep convolutional generative adversarial network_supp1-3288851.PDF. https://doi.org/10.1109/tai.2023.3288851/mm1
15. Goodfellow IJ, Pouget-Abadie J, Mirza M et al.(2014) Generative adversarial networks. arXiv:1406.2661. https://arxiv.org/abs/1406.2661
16. Zhu JY, Park T, Isola P et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In:IEEE international conference on computer vision (ICCV), Venice, Italy, pp 2242-2251
17. Liu MY, Tuzel O (2016) Coupled generative adversarial networks. arXiv:1606.07536. https://arxiv.org/abs/1606.07536
18. Zhang H, Goodfellow I, Metaxas D et al.(2019) Self-attention generative adversarial networks. arXiv:1805.08318. https://arxiv.org/abs/1805.08318
19. Brock A, Donahue J, Simonyan K (2019) Large scale gan training for high fidelity natural image synthesis. arXiv:1809.11096. https://arxiv.org/abs/1809.11096
20. Ma B, Wei X, Liu C et al (2020) Data augmentation in microscopic images for material data mining. NPJ Comput Mater 6(1):125. https://doi.org/10.1038/s41524-020-00392-6
21. Buehler EL, Buehler MJ (2022) End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks. Biomed Eng Adv 4:100038. https://doi.org/10.1016/j.bea.2022.100038
22. Liu F, Zhou S, Xia C et al (2016) Optimization of fatigue life distribution model and establishment of probabilistic S-N curves for a 165 KSI grade super high strength drill pipe steel. J Petrol Sci Eng 145:527-532
23. Li S, Xie X, Cheng C et al (2020) A modified coffin-manson model for ultra-low cycle fatigue fracture of structural steels considering the effect of stress triaxiality. Eng Fract Mech 237:107223. https://doi.org/10.1016/j.engfracmech.2020.107223
24. Farhat H (2021) Operation, maintenance, and repair of land-based gas turbines. Elsevier, Amsterdam
25. Cooper CV, Fine ME (1985) Fatigue microcrack initiation in polycrystalline alpha-iron with polished and oxidized surfaces. Metall Trans A 16(4):641-649
26. Baldi P, Sadowski P (2014) The dropout learning algorithm. ArtifIntell 210:78-122
27. Goh ATC (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9(3):143-151
28. Yin L, Zhang B (2021) Time series generative adversarial network controller for long-term smart generation control of microgrids. Appl Energ 281:116069. https://doi.org/10.1016/j.apenergy.2020.116069
29. Razmjoo A, Xanthopoulos P, Zheng QP (2017) Online feature importance ranking based on sensitivity analysis. Expert Syst Appl 85:397-406
30. Coli CA, Windmeijer FAG (1997) An R-squared measure of goodness of fit for some common nonlinear regression models. J Econom 77(2):329-342
31. Miao C, Li R, Yu J (2020) Effects of characteristic parameters of corrosion pits on the fatigue life of the steel wires. J Constr Steel Res 168:105879. https://doi.org/10.1016/j.jcsr.2019.105879
32. Parzinger M, Hanfstaengl L, Sigg F et al (2022) Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection-using machine learning methods random forest and lasso. Build Environ 223:109313. https://doi.org/10.1016/j.buildenv.2022.109313
33. Itabashi M, Kawata K (2000) Carbon content effect on high-strain-rate tensile properties for carbon steels. Int J Impact Eng 24(2):117-131
34. Rodrigues CAD, Bandeira RM, Duarte BB et al (2016) Effect of phosphorus content on the mechanical, microstructure and corrosion properties of supermartensitic stainless steel. Mat Sci Eng A 650:75-83
35. Meiners T, Peng Z, Gault B et al (2018) Sulfur-induced embrittlement in high-purity, polycrystalline copper. Acta Mater 156:64-75
36. Jain S, Jain P, Pandey K et al (2022) Artificial intelligence, machine learning, and mental health in pandemics. https://doi.org/10.1016/c2020-0-04085-5
37. He K, Zhang X, Ren S et al (2015) Deep residual learning for image recognition. arXiv:1512.03385. https://arxiv.org/abs/1512.03385
38. Yamashita R, Nishio M, Do RKG et al (2018) Convolutional neural networks:an overview and application in radiology. Insights Imaging 9(4):611-629
39. Ana Cláudia OES, de Souza MB, da Silva FV (2022) Exploring the potential of fully convolutional neural networks for FDD of a chemical process. Comput Aided Chem Eng 49:1621-1626
40. Kurek A, Kurek M,Łagoda T (2019) Stress-life curve for high and low cycle fatigue. J Theor App Mech 57(3):677-684
