Data-Driven Roll Pass Design of Wire Rod Mills

Abstract

The classical approach for roll pass design of a wire rod mill employs an iterative technique incorporating spread calculation and rectangular equivalent pass methods. This method comes to its limits in terms of computational efficiency and numerical stability when a complete pass design for a wire rod mill with lots of different final dimensions and materials must be designed. To improve the pass design technique, a fast data-driven method for pass design based on synthetic data generated by the classical pass design model was created. The results are compared to the original training data, as well as newly generated test data. It is shown that the artificial neural network (ANN) is able to predict appropriate oval groove geometries with good precision.