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

  • Xiaoxiao Geng
  • Hao WangEmail author
  • Asad Ullah
  • Weihua Xue
  • Song Xiang
  • Li Meng
  • Guang Ma
Machine Learning Applications in Advanced Manufacturing Processes
  • 2 Downloads

Abstract

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.

Notes

Acknowledgements

The authors acknowledge the financial support from the National Key Research and Development Program of China (No. 2017YFB0903901), the National Natural Science Foundation of China (No. 51571020), the Fundamental Research Funds for the Central Universities (Project No. FRF-IC-19-003), the State Key Laboratory for Advanced Metals and Materials (No. 2019Z-6) and the Fundamental Research Funds for the Liaoning Universities (LJ2017QL006).

Supplementary material

11837_2020_4057_MOESM1_ESM.pdf (443 kb)
Supplementary material 1 (PDF 443 kb)

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Copyright information

© The Minerals, Metals & Materials Society 2020

Authors and Affiliations

  • Xiaoxiao Geng
    • 1
  • Hao Wang
    • 1
    Email author
  • Asad Ullah
    • 2
  • Weihua Xue
    • 3
  • Song Xiang
    • 4
  • Li Meng
    • 5
  • Guang Ma
    • 6
  1. 1.School of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Department of Mathematical SciencesKarakoram International University GilgitGilgit-BaltistanPakistan
  3. 3.School of Materials Science and EngineeringLiaoning Technical UniversityFuxinChina
  4. 4.College of Materials and MetallurgyGuizhou UniversityGuiyangChina
  5. 5.Metallurgical Technology Institute, Central Iron and Steel Research InstituteBeijingChina
  6. 6.State Key Laboratory of Advanced Power Transmission TechnologyGlobal Energy Interconnection Research Institute Co., LtdBeijingChina

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