Prediction of Continuous Cooling Transformation Diagrams for Ni-Cr-Mo Welding Steels via Machine Learning Approaches

被引:21
作者
Geng, Xiaoxiao [1 ]
Wang, Hao [1 ]
Ullah, Asad [2 ]
Xue, Weihua [3 ]
Xiang, Song [4 ]
Meng, Li [5 ]
Ma, Guang [6 ]
机构
[1] Univ Sci & Technol Beijing, Sch Mat Sci & Engn, Beijing, Peoples R China
[2] Karakoram Int Univ Gilgit, Dept Math Sci, Gilgit Baltistan 15100, Pakistan
[3] Liaoning Tech Univ, Sch Mat Sci & Engn, Fuxing, Peoples R China
[4] Guizhou Univ, Coll Mat & Met, Guiyang, Peoples R China
[5] Cent Iron & Steel Res Inst, Met Technol Inst, Beijing, Peoples R China
[6] Global Energy Interconnect Res Inst Co Ltd, State Key Lab Adv Power Transmiss Technol, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
MODEL;
D O I
10.1007/s11837-020-04057-z
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Continuous cooling transformation diagrams in synthetic weld heat-affected zones (SH-CCT diagrams) are important tools to analyze the microstructure and mechanical properties of the heat-affected zone under certain welding conditions and to evaluate the weldability of steel. In this study, various machine-learning approaches are used to select an appropriate model for prediction of SH-CCT diagrams for Ni-Cr-Mo steels using relevant material descriptors including the chemical compositions and cooling rate. Random forest is the best model to predict the ferrite and bainite transition start temperature accurately, K-nearest neighbors is suitable for predicting the start temperature of martensite transformation, and random committee is used to predict the hardness. These optimal models are used to predict the SH-CCT diagrams of five kinds of steels to verify the accuracy. The results show that the predicted values of the optimal models agree well with the experimental data with a strong correlation coefficient and low error value.
引用
收藏
页码:3926 / 3934
页数:9
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