Prediction of strip section shape for hot-rolled based on mechanism fusion data model

Abstract

With the rise and vigorous development of artificial intelligence (AI) and data mining methodologies, machine learning (ML) has been successfully and widely used in industrial fields. For hot-rolled strips, both the rolling mechanism analyzing and multi-indicators integrated optimization are difficulties in hot rolling process. To overcome the limitations of multi-variable, nonlinear, and strong coupling in the process of hot rolling, and to improve the prediction accuracy of multi-output regression model, a novel model of rolling mechanism guided ML was developed. Mechanism data were calculated as added input features to participate in training process by way of dimensionality processing (DP). Simultaneously, genetic algorithm (GA) was adopted to realize multi-objective optimization of multi-output support vector regression (M-SVR) to further enhance the performance of model ultimately. Especially, the experimental results of root mean square error (RMSE) and correlation coefficient (R) of strip crown and thickness are 2.5218, 0.9891 and 2.1302, 0.9902, respectively, which fully demonstrates that this method does not limit the prediction accuracy but greatly improved the generalization ability.