Hot Ductility Prediction Model of Cast Steel with Low-Temperature Transformed Structure during Continuous Casting

Abstract

When various alloying elements are added or the cooling rate is increased, steel grades with U- or V-typed ductility behavior show N-shaped ductility behavior in which the ductility decreases in the low-temperature region. This study proposes a method that uses N-shaped data fitting and random forest to predict ductility behavior of steel grades that have bainite microstructure. To include the phenomenon in which that ductility decreases below the intermediate temperature, the data range was extended to temperature T T range in which the ductility decreases at T N-shaped data fitting method using six parameters was proposed. Comparison with the experimental values confirmed the effectiveness of the proposed model. Also, the model has better ability than models to predict bainite start temperature TBS. In a case study, the change of ductility behavior according to the cooling rate was observed for Nb-added steel. As the cooling rate increased from 1 °C/s to 10 °C/s, the formation of hard phase was relatively promoted, and different transformation behaviors appeared. This ability to predict the ductility behavior of alloy steels with a bainite microstructure, and to predict TBS below the intermediate temperature enables effective control of the secondary cooling conditions during continuous casting process, minimizing the formation of cracks on the slab surface.

Keywords

• surface crack
• bainite start temperature
• random forest
• machine learning
• <i>N</i>-shaped fitting