Page 25 - Read Online
P. 25

Page 18 of 20                           Li et al. J Mater Inf 2024;4:27  https://dx.doi.org/10.20517/jmi.2024.44

                   2016;9:331-7.  DOI
               7.       Mao C, Liu C, Yu L, Li H, Liu Y. Mechanical properties and tensile deformation behavior of a reduced activated ferritic-martensitic
                   (RAFM) steel at elevated temperatures. Mater Sci Eng A 2018;725:283-9.  DOI
               8.       Rowcliffe A, Garrison L, Yamamoto Y, Tan L, Katoh Y. Materials challenges for the fusion nuclear science facility. Fusion Eng Des
                   2018;135:290-301.  DOI
               9.       Rowcliffe A, Kessel C, Katoh Y, et al. Materials-engineering challenges for the fusion core and lifetime components of the fusion
                   nuclear science facility. Nucl Mater Energy 2018;16:82-7.  DOI
               10.      Cao H, Chen W. Effect of austenitizing temperature on microstructure and mechanical properties evaluation of microalloyed low-
                   carbon RAFM steel. Fusion Eng Des 2023;190:113645.  DOI
               11.      Nagasaka T, Sakasegawa H, Tanigawa H, et al. Tensile properties of F82H steel after aging at 400-650 °C for 100,000 h. Fusion Eng
                   Des 2015;98-9:2046-9.  DOI
               12.      Li Y, Nagasaka T, Muroga T, Huang Q, Wu Y. Effect of thermal ageing on tensile and creep properties of JLF-1 and CLAM steels. J
                   Nucl Mater 2009;386-8:495-8.  DOI
               13.      Zhong B, Huang B, Li C, et al. Creep deformation and rupture behavior of CLAM steel at 823 K and 873 K. J Nucl Mater
                   2014;455:640-4.  DOI
               14.      Sawada K, Kushima H, Tabuchi M, Kimura K. Microstructural degradation of Gr.91 steel during creep under low stress. Mater Sci
                   Eng A 2011;528:5511-8.  DOI
               15.      Mitsuhara M, Yamasaki S, Miake M, et al. Creep strengthening by lath boundaries in 9Cr ferritic heat-resistant steel. Phil Mag Lett
                   2016;96:76-83.  DOI
               16.      Xiao X, Liu G, Hu B, Wang J, Ma W. Coarsening behavior for M C  carbide in 12 %Cr-reduced activation ferrite/martensite steel:
                                                               23
                                                                 6
                   experimental study combined with DICTRA simulation. J Mater Sci 2013;48:5410-9.  DOI
               17.      Xu Y, Zhang X, Tian Y, et al. Study on the nucleation and growth of M C  carbides in a 10% Cr martensite ferritic steel after long-
                                                                  23  6
                   term aging. Mater Charact 2016;111:122-7.  DOI
               18.      Tan L, Byun T, Katoh Y, Snead L. Stability of MX-type strengthening nanoprecipitates in ferritic steels under thermal aging, stress
                   and ion irradiation. Acta Mater 2014;71:11-9.  DOI
               19.      Chen J, Liu C, Wei C, Liu Y, Li H. Effects of isothermal aging on microstructure and mechanical property of low-carbon RAFM steel.
                   Acta Metall Sin 2019;32:1151-60.  DOI
               20.      Xia Z, Zhang C, Yang Z. Control of precipitation behavior in reduced activation steels by intermediate heat treatment. Mater Sci Eng A
                   2011;528:6764-8.  DOI
               21.      Tan L, Snead L, Katoh Y. Development of new generation reduced activation ferritic-martensitic steels for advanced fusion reactors. J
                   Nucl Mater 2016;478:42-9.  DOI
               22.      Kim T, Kim T, Cho Y, et al. Influence of Ti addition on MX precipitation and creep-fatigue properties of RAFM steel for nuclear
                   fusion reactor. J Nucl Mater 2022;571:154001.  DOI
               23.      Mao C, Liu C, Yu L, Liu Y. Developing of containing Ta, Zr reduced activation ferritic/martensitic (RAFM) steel with excellent creep
                   property. Mater Sci Eng A 2022;851:143625.  DOI
               24.      Jun S, Kim T, Im S, et al. Atomic scale identification of nano-sized precipitates of Ta/Ti-added RAFM steel and its superior creep
                   strength. Mater Charact 2020;169:110596.  DOI
               25.      Zhou J, Shen Y, Xue W, Jia N, Misra R. Improving strength and ductility of low activation martensitic (LAM) steel by alloying with
                   titanium and tempering. Mater Sci Eng A 2021;799:140152.  DOI
               26.      Klueh R. Reduced-activation steels: future development for improved creep strength. J Nucl Mater 2008;378:159-66.  DOI
               27.      Tan L, Katoh Y, Snead L. Development of castable nanostructured alloys as a new generation RAFM steels. J Nucl Mater
                   2018;511:598-604.  DOI
               28.      Yuan R, Liu Z, Balachandran PV, et al. Accelerated discovery of large electrostrains in BaTiO -based piezoelectrics using active
                                                                                   3
                   learning. Adv Mater 2018;30:1702884.  DOI
               29.      Wen C, Zhang Y, Wang C, et al. Machine learning assisted design of high entropy alloys with desired property. Acta Mater
                   2019;170:109-17.  DOI
               30.      Yu J, Wang C, Chen Y, Wang C, Liu X. Accelerated design of L12-strengthened Co-base superalloys based on machine learning of
                   experimental data. Mater Design 2020;195:108996.  DOI
               31.      Wang C, Shen C, Cui Q, Zhang C, Xu W. Tensile property prediction by feature engineering guided machine learning in reduced
                   activation ferritic/martensitic steels. J Nucl Mater 2020;529:151823.  DOI
               32.      Li X, Zheng M, Yang X, Chen P, Ding W. A property-oriented design strategy of high-strength ductile RAFM steels based on machine
                   learning. Mater Sci Eng A 2022;840:142891.  DOI
               33.      Andersson J, Helander T, Höglund L, Shi P, Sundman B. Thermo-Calc & DICTRA, computational tools for materials science.
                   Calphad 2002;26:273-312.  DOI
               34.      Xiong J, Shi S, Zhang T. Machine learning of phases and mechanical properties in complex concentrated alloys. J Mater Sci Technol
                   2021;87:133-42.  DOI
               35.      Bobbili  R,  Ramakrishna  B.  Prediction  of  phases  in  high  entropy  alloys  using  machine  learning.  Mater  Today  Commun
                   2023;36:106674.  DOI
               36.      Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE
   20   21   22   23   24   25   26   27   28   29   30