A joint model for hot-rolled strip quality in advance prediction

Abstract

ABSTRACT Aiming at the problem that previous product quality prediction models cannot directly assess product quality before hot rolling, this paper presents a joint model for process parameters generation and quality prediction based on Conditional Generative Adversarial Nets and Artificial Neural Network. After generated actual process parameters by the generation module, this model would predict the quality of products ahead of schedule according to the generated parameters before hot rolling, which do not rely on inputting actual process parameter online anymore. Finally, the model has been trained and tested with the actual data from a certain hot rolling plant. The experimental results show that the generated process parameters agree well with actual production and quality prediction accuracy can meet the production requirements, which confirms the proposed model can be applied to simulate the actual rolling process and predict strip quality ahead of hot rolling production, providing a reference for the adjustment of planning and scheduling in the future.