@ARTICLE{Shin_Seung-Hyeok_Application_2021,
 author={Shin, Seung-Hyeok and Kim, Sang-Gyu and Hwang, Byoungchul},
 volume={vol. 66},
 number={No 3},
 journal={Archives of Metallurgy and Materials},
 pages={719-723},
 howpublished={online},
 year={2021},
 publisher={Institute of Metallurgy and Materials Science of Polish Academy of Sciences},
 publisher={Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences},
 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.},
 type={Article},
 title={Application of Artificial Neural Network to Predict the Tensile Properties of Dual-Phase Steels},
 URL={http://journals.pan.pl/Content/119241/PDF/AMM-2021-3-10-Hwang.pdf},
 doi={10.24425/amm.2021.136368},
 keywords={Artificial Neural Network (ANN), dual-phase steels, alloying element, microstructural factor, tensile properties},
}