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Artificial Neural Network for Modeling the Tensile Properties of Ferrite-Pearlite Steels: Relative Importance of Alloying Elements and Microstructural Factors

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Abstract

An artificial neural network (ANN) model was developed to predict the tensile properties as a function of alloying element and microstructural factor of ferrite-pearlite steels. The input parameters of the model were composed of alloying elements (Mn, Si, Al, Nb, Ti, and V) and microstructural factors (pearlite fraction, ferrite grain size, interlamellar spacing, and cementite thickness), while the output parameters of the model were yield strength and tensile strength. Although the ferrite-pearlite steels have complex relationships among the alloying elements, microstructural factors, and tensile properties, the ANN model predictions were found to be more accurate with experimental results than the existing equation model. In the present study the individual effect of input parameters on the tensile properties was quantitatively estimated with the help of the average index of the relative importance for alloying elements as well as microstructural factors. The ANN model attempted from the metallurgical points of view is expected to be useful for designing new steels having required mechanical properties.

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Acknowledgements

This work was supported by the Technology Innovation Program (Grant No. 10063488) funded by the Ministry of Trade, Industry and Energy (MOTIE) and the Basic Science Research Program through the National Research Foundation of Korea (NRF-2017R1A2B2009336). The authors would like to thank Drs. P.L. Narayana and Chan Hee Park of Korea Institute of Materials Science for the instruction of artificial neural network program.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea

    Tae-Woon Hong, Sang-In Lee & Byoungchul Hwang

  2. Center for Energy Materials Research, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea

    Jae-Hyeok Shim

  3. Department of Materials Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea

    Myoung-Gyu Lee

  4. Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea

    Joonho Lee

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  1. Tae-Woon Hong
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  2. Sang-In Lee
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  4. Myoung-Gyu Lee
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Correspondence to Joonho Lee or Byoungchul Hwang.

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Hong, TW., Lee, SI., Shim, JH. et al. Artificial Neural Network for Modeling the Tensile Properties of Ferrite-Pearlite Steels: Relative Importance of Alloying Elements and Microstructural Factors. Met. Mater. Int. 27, 3935–3944 (2021). https://doi.org/10.1007/s12540-021-00982-z

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  • DOI: https://doi.org/10.1007/s12540-021-00982-z

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