Predicting mechanical properties lower upper bound for cold-rolling strip by machine learning-based artificial intelligence

The mechanical properties serve as crucial quality indicators for cold-rolled strips. For a long time, the mechanical properties mechanism and data-driven models can't comprehensively consider sufficient factors to achieve high-accuracy prediction due to the "data-isolated island" between production lines. In this research, we introduce a multi-process collaborative platform based on the industrial internet system. This platform is designed to enable real-time collection of diverse and heterogeneous data from both upstream and downstream processes of cold rolling. On this basis, a novel mechanical properties interval prediction model is proposed using the sparrow search algorithm to optimize fast learning network under the LUBE framework. We trained the model by using a dataset collected from a large steel plant. Based on the rolling theory and Pearson correlation coefficient, 25 features are selected as the inputs of the prediction model. The experimental results and comparison show that the proposed model is feasible and outperforms other machine learning models, such as the artificial bee colony algorithm optimized extreme learning machine and back propagation neural network model.