Prediction of Spread in Steel Wire Rod Rolling: Transferable and Explainable Approach

Abstract

Spread is a major parameter in the steel wire rod rolling process since it is required for the calculation of material cross-sectional area and other rolling characteristics. Therefore, it is important to have a method to predict the spread with high accuracy and less computation time in wire rod rolling. In this study, multiple artificial intelligence (AI) methods including Multi-Layer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are employed to predict the spread. The 3D finite element (FE) analysis is used to generate the input data for the AI model and investigate the effect of different input parameters on the spread in one-stand and three-stand rolling setups of the wire rod rolling. The results demonstrate that the backward tension and the roll diameter are the most influencing parameters. Due to the use of dimensionless inputs and outputs, the model is independent of geometries and processing conditions which results in the transferability of the model. Furthermore, the ANFIS model provides some level of reasoning for the user by using a rule-based approach. Data fusion is also used to combine all outputs of the trained models and provide a single output for the prediction of spread in new data sets. The reasoning and transferability of the model result in the prediction of spread for a wide range of conditions in the steel wire rod rolling process. The generality and accuracy of the proposed approach are examined by comparing the results of the AI model with the FE analysis and experimental data obtained from the steelmaking company. The findings indicate that there is good agreement between the predicted and the measured values.