Application of artificial neural network to predict the tensile properties of dual-phase steels

Seung Hyeok Shin, Sang Gyu Kim, Byoungchul Hwang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)719-723
Number of pages5
JournalArchives of Metallurgy and Materials
Volume66
Issue number3
DOIs
StatePublished - 2021

Keywords

  • Alloying element
  • Artificial neural network (ANN)
  • Dual-phase steels
  • Microstructural factor
  • Tensile properties

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