Application of Finite‐Element Simulation and Neural Networks in the Prediction of Metal Flow Behavior during the Sizing Press Process

Abstract

A dog‐bone profile is developed by inhomogeneous deformation of a slab, whose parameters can be used to describe the metal flow rule during the width reduction process by a sizing press. The accuracy of a dog‐bone profile is essential to ensure the high width precision of the roughing process. However, the existing equipment cannot satisfy the requirements of online measurement. To solve this problem, herein, a prediction model for dog‐bone parameters based on the finite‐element simulation and a neural network is presented. The finite‐element simulation can reproduce the field production process, revealing variations in the dog‐bone profile parameters. In addition, based on the simulation data, a neural network model is constructed to predict the dog‐profile parameters. By adjusting the hyperparameters, the prediction accuracy of the constructed deep neural network model with two hidden layers is improved. For the dog‐bone profile parameters, the mean squared error is less than 3 mm, the mean absolute percentage error is less than 2%, the maximum absolute percentage error is less than 9%, and the root mean squared error is less than 4 mm. Thus, an accurate prediction of the dog‐bone profile parameters is achieved, which can guide subsequent high‐precision width control.