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Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors 期刊论文
MATERIALS, 2023, 卷号: 16, 期号: 1, 页码: 13./通讯作者：Sun, Chengqi
Authors:  Guo, Yiyun;  Rui SS(芮少石);  Xu, Wei;  Sun CQ(孙成奇)
Adobe PDF(9664Kb)  |  Favorite  |  View/Download：148/22  |  Submit date：2023/02/09
machine learning  nickel-based superalloy  fatigue strength prediction  temperature  stress ratio  
Effect of porosity on the crack pattern and residual strength of ceramics after quenching 期刊论文
JOURNAL OF MATERIALS SCIENCE, 2013, 卷号: 48, 期号: 18, 页码: 6431-6436
Authors:  Shao YF(邵颖峰);  Du RQ(杜睿琪);  Wu XF;  Song F(宋凡);  Xu XH(许向红);  Jiang CP;  Shao, YF (reprint author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China.
Adobe PDF(625Kb)  |  Favorite  |  View/Download：896/281  |  Submit date：2013/09/02
Alumina  Cracks  Porosity  Quenching  Thermal Shock  Minimum Potential Energy Principle  Pore Volume Fractions  Porous Alumina  Prediction-based  Residual Strength  Strength Reduction  Thermal Shock Resistance  Water-quenching