TY - JOUR
T1 - Three-step learning strategy for designing 15Cr ferritic steels with enhanced strength and plasticity at elevated temperature
AU - Hu, Xiaobing
AU - Chen, Yiming
AU - Lu, Jianlin
AU - Xing, Chen
AU - Zhao, Jiajun
AU - Wu, Qingfeng
AU - Jia, Yuhao
AU - Li, Junjie
AU - Wang, Zhijun
AU - Wang, Jincheng
N1 - Publisher Copyright:
© 2023
PY - 2023/11/20
Y1 - 2023/11/20
N2 - 15Cr ferrite steels are urgently required in advanced Ultra-supercritical power plants but meet design challenges in balancing excellent strength and plasticity at high temperatures. We developed a three-step learning strategy based on mutually driven machine learning and purposeful experiments to complete this multi-objective task. Compared with traditional adaptive learning and local-interpolation learning, this step-by-step modular manner provides good transparency and interpretability of the information flow, which is ensured by identifying essential factors from an exquisitely prepared composition-microstructure dataset, and learning valuable knowledge about the composition-property relationship. The requirement of only two groups of experiments indicates the low cost and high efficiency of the strategy. Performing the strategy, we found that Ti is another key element affecting the Laves phase besides Mo and W, and their effects on ultimate tensile strength (UTS) and elongation were also uncovered. Importantly, several low-cost steels free of Co were successfully designed, and the best steel exhibited 156%, 31%, and 62% higher UTS and elongation at 650 °C than the typical 9Cr, 15Cr, and 20Cr steels, respectively. Based on the advantages and success of the strategy in terms of alloy improvement, we believe the strategy suits other multi-objective design tasks in more materials systems.
AB - 15Cr ferrite steels are urgently required in advanced Ultra-supercritical power plants but meet design challenges in balancing excellent strength and plasticity at high temperatures. We developed a three-step learning strategy based on mutually driven machine learning and purposeful experiments to complete this multi-objective task. Compared with traditional adaptive learning and local-interpolation learning, this step-by-step modular manner provides good transparency and interpretability of the information flow, which is ensured by identifying essential factors from an exquisitely prepared composition-microstructure dataset, and learning valuable knowledge about the composition-property relationship. The requirement of only two groups of experiments indicates the low cost and high efficiency of the strategy. Performing the strategy, we found that Ti is another key element affecting the Laves phase besides Mo and W, and their effects on ultimate tensile strength (UTS) and elongation were also uncovered. Importantly, several low-cost steels free of Co were successfully designed, and the best steel exhibited 156%, 31%, and 62% higher UTS and elongation at 650 °C than the typical 9Cr, 15Cr, and 20Cr steels, respectively. Based on the advantages and success of the strategy in terms of alloy improvement, we believe the strategy suits other multi-objective design tasks in more materials systems.
KW - 15Cr ferrite steels
KW - Machine learning
KW - Multi-objective design
KW - Strength and plasticity trade-off
UR - http://www.scopus.com/inward/record.url?scp=85162852863&partnerID=8YFLogxK
U2 - 10.1016/j.jmst.2023.04.034
DO - 10.1016/j.jmst.2023.04.034
M3 - 文章
AN - SCOPUS:85162852863
SN - 1005-0302
VL - 164
SP - 79
EP - 94
JO - Journal of Materials Science and Technology
JF - Journal of Materials Science and Technology
ER -