ORIGINAL ARTICLE
Data-driven prediction of the probability of creep–fatigue crack initiation in 316H stainless steel
Saeed Zare Chavoshi,
Vito L. Tagarielli,
Saeed Zare Chavoshi
Department of Materials, University of Oxford, Oxford, UK
Search for more papers by this authorCorresponding Author
Vito L. Tagarielli
Department of Aeronautics, Imperial College London, London, UK
Correspondence
Vito L. Tagarielli, Department of Aeronautics, Imperial College London, London SW7 2AZ, UK.
Email: [email protected]
Search for more papers by this authorSaeed Zare Chavoshi,
Vito L. Tagarielli,
Saeed Zare Chavoshi
Department of Materials, University of Oxford, Oxford, UK
Search for more papers by this authorCorresponding Author
Vito L. Tagarielli
Department of Aeronautics, Imperial College London, London, UK
Correspondence
Vito L. Tagarielli, Department of Aeronautics, Imperial College London, London SW7 2AZ, UK.
Email: [email protected]
Search for more papers by this authorAbstract
Stainless steel components in advanced gas-cooled reactors (AGRs) are susceptible to creep–fatigue cracking at high temperatures. Quantifying the probability of creep–fatigue crack initiation requires probabilistic numerical simulations; these are complex and computationally intensive. Here, we present a data-driven approach to develop fast probabilistic surrogate models of creep–fatigue crack initiation in 316H stainless steel. We perform a set of Monte Carlo simulations based on the R5V2/3 high temperature assessment procedure and determine the sensitivity of the probability of crack initiation to loads and operating conditions. The data are used to train different supervised machine learning models considering Bayesian hyperparameter optimization. We discuss the relative performance of such models and show that a gradient tree boosting algorithm results in surrogate models with the highest accuracy.
Highlights
- A data-driven approach to predict failure probability is introduced.
- Monte Carlo simulations based on the R5V2/3 are performed.
- Gradient tree boosting algorithm results in surrogate models with the highest accuracy.
- Operating temperature accounts for more than 60% of the regression power in the surrogate model.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Supporting Information
| Filename | Description |
|---|---|
| ffe13858-sup-0001-3_Supplementary Material.pdfPDF document, 543.2 KB |
Table S1. Levels of the input descriptors used to generate 560 instances considering a full factorial design of experiment. Table S2. 560 Instances of probability of creep-fatigue crack initiation. Table S3. Optimal hyperparameters obtained by Bayesian optimisation technique. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- 1Chavoshi SZ, Booker J, Bradford R, Martin M. A review of probabilistic structural integrity assessment in the nuclear sector and possible future directions. Fatigue Fract Eng Mater Struct. 2021; 44(12): 3227-3257.
- 2Chavoshi SZ, Bradford R, Booker J. A validated approach for probabilistic structural integrity design code implementation. Eng Fract Mech. 2021; 257:108028.
- 3Bradford R, Holt P. Application of probabilistic modelling to the lifetime management of nuclear boilers in the creep regime: part 2. Int J Press Vessel Pip. 2013; 111-112: 232-245.
- 4Holt P, Bradford R. Application of probabilistic modelling to the lifetime management of nuclear boilers in the creep regime: part 1. Int J Press Vessel Pip. 2012; 95: 48-55.
- 5Chavoshi SZ, Tagarielli VL, Zhao L, Nikbin K. Finite element analysis of creep-fatigue-oxidation interactions in 316H stainless steel. Eng Fail Anal. 2020; 116:104709.
- 6Chavoshi SZ, Hill LT, Bagnoli KE, Holloman RL, Nikbin KM. A combined fugacity and multi-axial ductility damage approach in predicting high temperature hydrogen attack in a reactor inlet nozzle. Eng Fail Anal. 2020; 117:104948.
- 7Kroese DP, Taimre T, Botev ZI. Handbook of Monte Carlo Methods. Vol. 706. John Wiley & Sons; 2013.
- 8Chavoshi SZ. Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite. Prod Eng. 2011; 5(1): 37-47.
10.1007/s11740-010-0282-x Google Scholar
- 9Chavoshi SZ. Analysis and predictive modeling of performance parameters in electrochemical drilling process. Int J Adv Manuf Technol. 2011; 53(9): 1081-1101.
- 10Chavoshi SZ, Tajdari M. Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool. Int J Mater Form. 2010; 3(4): 233-239.
- 11Tajdari M, Chavoshi S. Prediction and analysis of radial overcut in holes drilled by electrochemical machining process. Open Eng. 2013; 3(3): 466-474.
10.2478/s13531-011-0074-x Google Scholar
- 12Yazdi MRS, Chavoshi SZ. Analysis and estimation of state variables in CNC face milling of AL6061. Prod Eng. 2010; 4(6): 535-543.
10.1007/s11740-010-0232-7 Google Scholar
- 13Zare CS. Modelling of surface roughness in CNC face milling of alloy stellite 6. Int J Comput Mater Sci Surf Eng. 2013; 5(4): 304-321.
- 14Wang C, Tan X, Tor SB, Lim C. Machine learning in additive manufacturing: state-of-the-art and perspectives. Addit Manuf. 2020; 36:101538.
- 15Sacco C, Radwan AB, Anderson A, Harik R, Gregory E. Machine learning in composites manufacturing: a case study of automated fiber placement inspection. Compos Struct. 2020; 250:112514.
- 16Pilania G, Wang C, Jiang X, Rajasekaran S, Ramprasad R. Accelerating materials property predictions using machine learning. Sci Rep. 2013; 3(1): 1-6.
- 17Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: recent applications and prospects. Npj Comput Mater. 2017; 3(1): 1-13.
- 18Vlassis NN, Ma R, Sun W. Geometric deep learning for computational mechanics part I: anisotropic Hyperelasticity. Comput Methods Appl Mech Eng. 2020; 371:113299.
- 19Logarzo HJ, Capuano G, Rimoli JJ. Smart constitutive laws: inelastic homogenization through machine learning. Comput Methods Appl Mech Eng. 2021; 373:113482.
- 20Ge W, Tagarielli VL. A computational framework to establish data-driven constitutive models for time-or path-dependent heterogeneous solids. Sci Rep. 2021; 11(1): 1-18.
- 21Zhang M, Sun C-N, Zhang X, et al. High cycle fatigue life prediction of laser additive manufactured stainless steel: a machine learning approach. Int J Fatigue. 2019; 128:105194.
- 22Bao H, Wu S, Wu Z, Kang G, Peng X, Withers PJ. A machine-learning fatigue life prediction approach of additively manufactured metals. Eng Fract Mech. 2021; 242:107508.
- 23Barbosa JF, Correia JA, Júnior RF, De Jesus AM. Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network. Int J Fatigue. 2020; 135:105527.
- 24Jimenez-Martinez M, Alfaro-Ponce M. Fatigue damage effect approach by artificial neural network. Int J Fatigue. 2019; 124: 42-47.
- 25Chen J, Liu Y. Probabilistic physics-guided machine learning for fatigue data analysis. Expert Syst Appl. 2021; 168:114316.
- 26Zhan Z, Li H. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L. Int J Fatigue. 2021; 142:105941.
- 27Gao J, Wang C, Xu Z, Wang J, Yan S, Wang Z. Gaussian process regression based remaining fatigue life prediction for metallic materials under two-step loading. Int J Fatigue. 2022; 158:106730.
- 28Lian Z, Li M, Lu W. Fatigue life prediction of aluminum alloy via knowledge-based machine learning. Int J Fatigue. 2022; 157:106716.
- 29Gan L, Zhao X, Wu H, Zhong Z. Estimation of remaining fatigue life under two-step loading based on kernel-extreme learning machine. Int J Fatigue. 2021; 148:106190.
- 30Choi J, Quagliato L, Lee S, Shin J, Kim N. Multiaxial fatigue life prediction of polychloroprene rubber (CR) reinforced with tungsten nano-particles based on semi-empirical and machine learning models. Int J Fatigue. 2021; 145:106136.
- 31Yang J, Kang G, Liu Y, Kan Q. A novel method of multiaxial fatigue life prediction based on deep learning. Int J Fatigue. 2021; 151:106356.
- 32Mortazavi S, Ince A. An artificial neural network modeling approach for short and long fatigue crack propagation. Comput Mater Sci. 2020; 185:109962.
- 33Ma X, He X, Tu Z. Prediction of fatigue–crack growth with neural network-based increment learning scheme. Eng Fract Mech. 2020; 241:107402.
- 34Rovinelli A, Sangid MD, Proudhon H, Guilhem Y, Lebensohn RA, Ludwig W. Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: in-situ experiments and crystal plasticity simulations. J Mech Phys Solids. 2018; 115: 208-229.
- 35Do DT, Lee J, Nguyen-Xuan H. Fast evaluation of crack growth path using time series forecasting. Eng Fract Mech. 2019; 218:106567.
- 36Liu Y, Wu J, Wang Z, et al. Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning. Acta Mater. 2020; 195: 454-467.
- 37Hong H, Cai Z, Wang H, Wang WZ, Liu Y. A model-guided neural network for the prediction of creep behavior under in-service conditions. J Eng Gas Turbines Power. 2020; 142(7):071008.
- 38Shin D, Yamamoto Y, Brady MP, Lee S, Haynes JA. Modern data analytics approach to predict creep of high-temperature alloys. Acta Mater. 2019; 168: 321-330.
- 39Peng J, Yamamoto Y, Brady MP, Lee S, Haynes JA, Shin D. Uncertainty quantification of machine learning predicted creep property of alumina-forming austenitic alloys. JOM. 2021; 73(1): 164-173.
- 40Biswas S, Castellanos DF, Zaiser M. Prediction of creep failure time using machine learning. Sci Rep. 2020; 10(1): 1-11.
- 41Srinivasan V, Valsan M, Rao KBS, Mannan S, Raj B. Low cycle fatigue and creep–fatigue interaction behavior of 316L (N) stainless steel and life prediction by artificial neural network approach. Int J Fatigue. 2003; 25(12): 1327-1338.
- 42Venkatesh V, Rack H. A neural network approach to elevated temperature creep–fatigue life prediction. Int J Fatigue. 1999; 21(3): 225-234.
- 43Song L-K, Bai G-C, Fei C-W. Dynamic surrogate modeling approach for probabilistic creep-fatigue life evaluation of turbine disks. Aerosp Sci Technol. 2019; 95:105439.
- 44Song L-K, Bai G-C, Fei C-W. Probabilistic LCF life assessment for turbine discs with DC strategy-based wavelet neural network regression. Int J Fatigue. 2019; 119: 204-219.
- 45Song L-K, Bai G-C, Li X-Q. A novel metamodeling approach for probabilistic LCF estimation of turbine disk. Eng Fail Anal. 2021; 120:105074.
- 46Gu H-H, Wang R-Z, Zhu S-P, et al. Machine learning assisted probabilistic creep-fatigue damage assessment. Int J Fatigue. 2022; 156:106677.
- 47Guo K, Yan H, Huang D, Yan X. Active learning-based KNN-Monte Carlo simulation on the probabilistic fracture assessment of cracked structures. Int J Fatigue. 2022; 154:106533.
- 48 EDF Energy. R5 Issue 3, Assessment procedure for the high temperature response of structures. In: Volume 2/3: Creep-Fatigue Crack Initiation Procedure for Defect-free Structures.
- 49McKay MD, Beckman RJ, Conover WJ. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Dent Tech. 2000; 42(1): 55-61.
- 50Moffat A. Creep deformation model for parent 316H stainless steel. In: EDF Energy Internal Report, FNC 47354/43103R; 2015.
- 51Spindler MW. An interim upper bound stress modified ductility exhaustion model for type 316H. In: EDF Energy Internal Report, E/EAN/BBGB/0061/AGR/13; 2013.
- 52Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011; 12: 2825-2830.
- 53Vu Q-V, Truong V-H, Thai H-T. Machine learning-based prediction of CFST columns using gradient tree boosting algorithm. Compos Struct. 2021; 259:113505.
- 54Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001; 29(5): 1189-1232.
- 55Kuhn M, Johnson K. Applied Predictive Modeling. Vol. 26. Springer; 2013.
10.1007/978-1-4614-6849-3 Google Scholar
- 56 Linear model. scikit-learn.org. https://scikit-learn.org/stable/modules/linear_model.html
- 57Owen AB. A robust hybrid of lasso and ridge regression. Contemp Math. 2007; 443(7): 59-72.
10.1090/conm/443/08555 Google Scholar
- 58Zhang T. Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Paper Presented at: Proceedings of the Twenty-first International Conference on Machine Learning; 2004.
10.1145/1015330.1015332 Google Scholar
- 59 Stochastic gradient descent. scikit-learn.org. https://scikit-learn.org/stable/modules/sgd.html#id16
- 60Breiman L. Random forests. Mach Learn. 2001; 45(1): 5-32.
- 61Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression Trees. CRC Press; 1984.
- 62Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat Comput. 2004; 14(3): 199-222.
- 63McClelland JL, Rumelhart DE, Group PR. Parallel Distributed Processing. Vol. 2. MIT Press; 1986.
