References
- G.B. Olson, Genomic materials design: the ferrous frontier, Acta Mater. 61 (2013) 771-781. https://doi.org/10.1016/j.actamat.2012.10.045
- C. Wang, C. Zhang, Z. Yang, J. Su, Y. Weng, Multi-scale simulation of hydrogen influenced critical stress intensity in high CoeNi secondary hardening steel, Mater. Des. 87 (2015) 501-506. https://doi.org/10.1016/j.matdes.2015.08.040
- C. Wang, C. Zhang, Z. Yang, J. Su, Y. Weng, Microstructure analysis and yield strength simulation in high CoeNi secondary hardening steel, Mater. Sci. Eng. A 669 (2016) 312-317.
- C. Wang, C. Zhang, Z. Yang, J. Zhao, Multiscale simulation of yield strength in reduced-activation ferritic/martensitic steel, Nucl. Eng. Technol. 49 (2017) 569-575. https://doi.org/10.1016/j.net.2016.10.006
- J.S. Wang, M.D. Mulholland, G.B. Olson, D.N. Seidman, Prediction of the yield strength of a secondary-hardening steel, Acta Mater. 61 (2013) 4939-4952. https://doi.org/10.1016/j.actamat.2013.04.052
- S. Datta, F. Pettersson, S. Ganguly, H. Saxen, N. Chakraborti, Designing High strength multi-phase steel for improved strengtheductility balance using neural networks and multi-objective genetic algorithms, ISIJ Int. 47 (2007) 1195-1203. https://doi.org/10.2355/isijinternational.47.1195
- S. Datta, F. Pettersson, S. Ganguly, H. Saxen, N. Chakraborti, Identification of factors governing mechanical properties of TRIP-aided steel using genetic algorithms and neural networks, Mater. Manuf. Process. 23 (2008) 130-137. https://doi.org/10.1080/10426910701774528
- S. Ganguly, S. Datta, N. Chakraborti, Genetic algorithms in optimization of strength and ductility of low-carbon steels, Mater. Manuf. Process. 22 (2007) 650-658. https://doi.org/10.1080/10426910701323607
- S. Ganguly, S. Datta, N. Chakraborti, Genetic algorithm-based search on the role of variables in the work hardening process of multiphase steels, Comput. Mater. Sci. 45 (2009) 158-166. https://doi.org/10.1016/j.commatsci.2008.01.074
- C. Wang, C. Zhang, Z. Yang, J. Su, Y. Weng, Analysis of fracture toughness in high CoeNi secondary hardening steel using FEM, Mater. Sci. Eng. A 646 (2015) 1-7. https://doi.org/10.1016/j.msea.2015.08.003
- A. Bernieri, G. Betta, L. Ferrigno, M. Laracca, S. Mastrostefano, Multifrequency excitation and support vector machine regressor for ECT defect characterization, IEEE Trans. Instrum. Meas. 63 (2014) 1272-1280. https://doi.org/10.1109/TIM.2013.2292326
- Z. Han, Y. Liu, J. Zhao, W. Wang, Real time prediction for converter gas tank levels based on multi-output least square support vector regressor, Contr. Eng. Pract. 20 (2012) 1400-1409. https://doi.org/10.1016/j.conengprac.2012.08.006
- R. Kemp, G.A. Cottrell, H.K.D.H. Bhadeshia, G.R. Odette, T. Yamamoto, H. Kishimoto, Neural-network analysis of irradiation hardening in lowactivation steels, J. Nucl. Mater. 348 (2006) 311-328. https://doi.org/10.1016/j.jnucmat.2005.09.022
- F.Y. Lu, Z.Q. Yin, C. Wang, C.H. Cui, J. Teng, S. Wang, W. Chen, W. Huang, B.J. Xu, G.C. Guo, Z.F. Han, Parameter optimization and real-time calibration of a measurement-device-independent quantum key distribution network based on a back propagation artificial neural network, J. Opt. Soc. Am. B 36 (2019) B92-B98. https://doi.org/10.1364/josab.36.000b92
- J. Ding, Z. Bar-Joseph, MethRaFo: MeDIP-seq methylation estimate using a random forest regressor, Bioinformatics 33 (2017) 3477-3479. https://doi.org/10.1093/bioinformatics/btx449
- S.F. Long, M. Zhao, X.F. He, Yield stress prediction model of RAFM steel based on the improved GDM-SA-SVR algorithm, Comput. Mater. Continua (CMC) 58 (2019) 727-760.
- M.M. Jin, P.H. Cao, M.P. Short, Predicting the onset of void swelling in irradiated metals with machine learning, J. Nucl. Mater. 523 (2019) 189-197.
- Q. Lu, S. van der Zwaag, W. Xu, High-throughput design of low-activation, high-strength creep-resistant steels for nuclear-reactor applications, J. Nucl. Mater. 469 (2016) 217-222. https://doi.org/10.1016/j.jnucmat.2015.11.052
- S. Chen, L. Rong, Effect of silicon on the microstructure and mechanical properties of reduced activation ferritic/martensitic steel, J. Nucl. Mater. 459 (2015) 13-19.
- P. Fernandez, A.M. Lancha, J. Lapena, M. Hernandez-Mayoral, Metallurgical characterization of the reduced activation ferritic/martensitic steel Eurofer'97 on as-received condition, Fusion Eng. Des. 58-59 (2001) 787-792. https://doi.org/10.1016/S0920-3796(01)00563-4
- R.L. Klueh, D.J. Alexander, M.A. Sokolov, Effect of chromium, tungsten, tantalum, and boron on mechanical properties of 5-9Cr-WVTaB steels, J. Nucl. Mater. 304 (2002) 139-152. https://doi.org/10.1016/S0022-3115(02)00885-1
- R.L. Klueh, J.J. Kai, D.J. Alexander, Microstructure mechanical-properties correlation of irradiated conventional and reduced-activation martensitic steels, J. Nucl. Mater. 225 (1995) 175-186. https://doi.org/10.1016/0022-3115(95)00061-5
- C.H. Lee, J.Y. Park, W.K. Seol, J. Moon, T.H. Lee, N.H. Kang, H.C. Kim, Microstructure and tensile and charpy impact properties of reduced activation ferriticemartensitic steel with Ti, Fusion Eng. Des. 124 (2017) 953-957. https://doi.org/10.1016/j.fusengdes.2017.05.085
- R. Ma, Y. Yang, Q. Yan, Y. Yang, X. Li, C. Ge, Effect of alloying on the properties of 9Cr low activation martensitic steels, Acta Metall. Sin. 23 (2010) 451-460.
- M.G. Park, C.H. Lee, J. Moon, J.Y. Park, T.H. Lee, N. Kang, H.C. Kim, Effect of microstructural evolution by isothermal aging on the mechanical properties of 9Cr-1WVTa reduced activation ferritic/martensitic steels, J. Nucl. Mater. 485 (2017) 15-22.
- A. Puype, L. Malerba, N. De Wispelaere, R. Petrov, J. Sietsma, Effect of W and N on mechanical properties of reduced activation ferritic/martensitic EUROFERbased steel grades, J. Nucl. Mater. 502 (2018) 282-288.
- L. Tan, L.L. Snead, Y. Katoh, Development of new generation reduced activation ferritic-martensitic steels for advanced fusion reactors, J. Nucl. Mater. 478 (2016) 42-49. https://doi.org/10.1016/j.jnucmat.2016.05.037
- J. Vanaja, K. Laha, M. Nandagopal, S. Sam, M.D. Mathew, T. Jayakumar, E. Rajendra Kumar, Effect of tungsten on tensile properties and flow behaviour of RAFM steel, J. Nucl. Mater. 433 (2013) 412-418. https://doi.org/10.1016/j.jnucmat.2012.10.040
- P. Wang, J. Chen, H. Fu, S. Liu, X. Li, Z. Xu, Effect of N on the precipitation behaviours of the reduced activation ferritic/martensitic steel CLF-1 after thermal ageing, J. Nucl. Mater. 442 (2013) S9-S12. https://doi.org/10.1016/j.jnucmat.2013.03.081
- H. Han, R. Yu, B. Li, Y. Zhang, Multi-objective optimization of corrugated tube inserted with multi-channel twisted tape using RSM and NSGA-II, Appl. Therm. Eng. 159 (2019).
- M.S. Mohammed, R.A. Vural, NSGA-II plus FEM based loss optimization of three-phase transformer, IEEE Trans. Ind. Electron. 66 (2019) 7417-7425. https://doi.org/10.1109/tie.2018.2881935
- S. Peng, T. Li, J. Zhao, S. Lv, G.Z. Tan, M. Dong, H. Zhang, Towards energy and material efficient laser cladding process: modeling and optimization using a hybrid TS-GEP algorithm and the NSGA-II, J. Clean. Prod. 227 (2019) 58-69.
- B. Sen, S.A.I. Hussain, M. Mia, U.K. Mandal, S.P. Mondal, Selection of an ideal MQL-assisted milling condition: an NSGA-II-coupled TOPSIS approach for improving machinability of Inconel 690, Int. J. Adv. Manuf. Technol. 103 (2019) 1811-829. https://doi.org/10.1007/s00170-019-03620-6
- C. Wang, C. Zhang, J. Zhao, Z. Yang, W. Liu, Microstructure evolution and yield strength of CLAM steel in low irradiation condition, Mater. Sci. Eng. A 682 (2017) 563-568.
- W. Wang, S. Liu, G. Xu, B. Zhang, Q. Huang, Effect of thermal aging on microstructure and mechanical properties of China low-activation martensitic steel at 550 degrees C, Nucl. Eng. Technol. 48 (2016) 518-524. https://doi.org/10.1016/j.net.2015.11.004
- S.N. Zhu, C. Zhang, Z.G. Yang, C.C. Wang, Hydrogen's influence on reduced activation ferritic/martensitic steels' elastic properties: density functional theory combined with experiment, Nucl. Eng. Technol. 49 (2017) 1748-1751. https://doi.org/10.1016/j.net.2017.08.021
- D. Shin, Y. Yamamoto, M.P. Brady, S. Lee, J.A. Haynes, Modern data analytics approach to predict creep of high-temperature alloys, Acta Mater. 168 (2019) 321-330. https://doi.org/10.1016/j.actamat.2019.02.017
- A. Panda, R. Naskar, S. Pal, Deep learning approach for segmentation of plain carbon steel microstructure images, IET Image Process. 13 (2019) 1516-1524.
- Z. Xiong, Y.X. Cui, Z.H. Liu, Y. Zhao, M. Hu, J.J. Hu, Evaluating explorative prediction power of machine learning algorithms for materials discovery using -fold forward cross-validation, Comput. Mater. Sci. 171 (2020), 109203.
Cited by
- Exploration of Machine Learning to Predict Hot Ductility of Cast Steel from Chemical Composition and Thermal Conditions vol.27, pp.2, 2020, https://doi.org/10.1007/s12540-020-00713-w
- High-throughput map design of creep life in low-alloy steels by integrating machine learning with a genetic algorithm vol.213, 2020, https://doi.org/10.1016/j.matdes.2021.110326