Process-structure-microstructure Relationship in Hot Strip Rolling of Steels Using Statistical Data Mining

AbstractMathematical models have been widely used for prediction of microstructure and mechanical properties in hot rolling of strip. To accurately predict these characteristics, it is necessary to create models that can replicate thermomechanical state of material and its evolution during processing. This paper presents development of a hybrid model that uses mills setting and real time plant data such as chemical composition; forces and temperatures; and integrates them with empirical relationships of material evolution to predict quality attributes. This information is combined with non-linear statistical data mining models to create online tool that predicts properties of individual coil. Case study from Steel Plant is presented that illustrates implementation, calibration and validation of this model across different materials grades