Archives of Metallurgy and Materials

Institute of Metallurgy and Materials Science of Polish Academy of Sciences
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Application of Artificial Neural Network to Predict the Tensile Properties of Dual-Phase Steels

  • Seung-Hyeok Shin (Seoul National University of Science and Technology, Department of Materials Science and Engineering) ;
  • Sang-Gyu Kim (Seoul National University of Science and Technology, Department of Materials Science and Engineering) ;
  • Byoungchul Hwang (Seoul National University of Science and Technology, Department of Materials Science and Engineering)
  • Published : 20210000
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Abstract

An artificial neural network (ANN) model was developed to predict the tensile properties of dual-phase steels in terms of alloying elements and microstructural factors. The developed ANN model was confirmed to be more reasonable than the multiple linear regression model to predict the tensile properties. In addition, the 3D contour maps and an average index of the relative importance calculated by the developed ANN model, demonstrated the importance of controlling microstructural factors to achieve the required tensile properties of the dual-phase steels. The ANN model is expected to be useful in understanding the complex relationship between alloying elements, microstructural factors, and tensile properties in dual-phase steels.

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Acknowledgement

This study was supported by the Technology Innovation Program (Grant No. 10063488) funded by the Ministry of Trade, Industry and Energy (MOTIE), South Korea, and the Basic Science Research Program, National Research Foundation of Korea (NRF-2017R1A2B2009336)