TY - JOUR
T1 - Artificial Neural Network for Modeling the Tensile Properties of Ferrite-Pearlite Steels
T2 - Relative Importance of Alloying Elements and Microstructural Factors
AU - Hong, Tae Woon
AU - Lee, Sang In
AU - Shim, Jae Hyeok
AU - Lee, Myoung Gyu
AU - Lee, Joonho
AU - Hwang, Byoungchul
N1 - Funding Information:
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.
Funding Information:
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.
Publisher Copyright:
© 2021, The Korean Institute of Metals and Materials.
PY - 2021/10
Y1 - 2021/10
N2 - 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. Graphic abstract: [Figure not available: see fulltext.]
AB - 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. Graphic abstract: [Figure not available: see fulltext.]
KW - Alloying element
KW - Artificial neural network
KW - Ferrite-pearlite steels
KW - Index of relative importance
KW - Microstructural factor
KW - Tensile property
UR - http://www.scopus.com/inward/record.url?scp=85101784878&partnerID=8YFLogxK
U2 - 10.1007/s12540-021-00982-z
DO - 10.1007/s12540-021-00982-z
M3 - Article
AN - SCOPUS:85101784878
VL - 27
SP - 3935
EP - 3944
JO - Metals and Materials International
JF - Metals and Materials International
SN - 1598-9623
IS - 10
ER -