Hong, Tae-Woon Lee, Sang-In Shim, Jae-Hyeok Lee, Myoung-Gyu Lee, Joonho Hwang, Byoungchul 2024-01-19T13:34:21Z 2024-01-19T13:34:21Z 2021-10-21 2021-10 1598-9623 https://pubs.kist.re.kr/handle/201004/116362 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. English KOREAN INST METALS MATERIALS Artificial Neural Network for Modeling the Tensile Properties of Ferrite-Pearlite Steels: Relative Importance of Alloying Elements and Microstructural Factors Article 10.1007/s12540-021-00982-z 1 METALS AND MATERIALS INTERNATIONAL, v.27, no.10, pp.3935 - 3944 METALS AND MATERIALS INTERNATIONAL 27 10 3935 3944 scie scopus kci ART002762497 000622219400002 2-s2.0-85101784878 Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science Metallurgy & Metallurgical Engineering Article MECHANICAL-PROPERTIES PROCESSING PARAMETERS FLOW-STRESS STRAIN-RATE GRAIN-SIZE BEHAVIOR PRECIPITATION TEMPERATURE PREDICTION TRANSFORMATION Ferrite-pearlite steels Artificial neural network Index of relative importance Alloying element Microstructural factor Tensile property