Modeling of Rolling Force for Thick Plate of Multicomponent Alloys and Its Application on Thickness Prediction

Abstract

During the rolling process of thick plate, the nonlinear specific plastic power that derived from the non-linear Mises yield criterion is difficult to be integrated, which has restricted the establishment of a rolling force model. To solve this problem, a new yield criterion is firstly established, and then used to derive a linear specific plastic power. Meanwhile, a kinematically admissible velocity field whose horizontal velocity component obeys the Logistic function is proposed to describe the metal flow of the deformed plate. On these bases, the rolling energy items including the internal deformation power of the deformed body, friction power on the contact surface, and shear power on the entry and exit sections are integrated successively, and the rolling force model is established. It is proved that the model can predict the rolling force well when compared with the actual data of multicomponent alloys. Besides, the formula for predicting the outlet thickness is ultimately given upon this derived model, and a good agreement is also found between the predicted values and the actual ones, since the absolute errors between them are within 0.50 mm.

Application of Machine Learning Methods for the Prediction of Roll Force and Torque during Plate Rolling of Micro-alloyed Steel

“…During the rolling thicker plate, the analytical equations are difficult to be integrated, restricting the establishment of an accurate rolling force model. A new yield criterion and new velocity field are proposed to improve the prediction accuracy during thicker plate rolling [25]. The maximum error in predicting the roll force for thicker plates was found to be around 12%.…”