A comprehensive performance evaluation method based on muti-task learning-assisted stacked performance-related autoencoder for hot strip mill process

In the context of intelligent manufacturing, the modern hot strip mill process (HSMP) shows characteristics such as diversification of products, multi-specification batch production, and demand-oriented customization. These characteristics pose significant challenges to ensuring process stability and consistency of product performance. Therefore, exploring the potential relationship between product performance and the production process, and developing a comprehensive performance evaluation method adapted to modern HSMP have become an urgent issue. A comprehensive performance evaluation method for HSMP by integrating multi-task learning and stacked performance-related autoencoder is proposed to solve the problems such as incomplete performance indicators (PIs) data, insufficient real-time acquisition requirements, and coupling of multiple PIs. First, according to the existing Chinese standards, a comprehensive performance evaluation grade strategy for strip steel is designed. The random forest model is established to predict and complete the parts of PIs data that could not be obtained in real-time. Second, a stacked performance-related autoencoder (SPAE) model is proposed to extract the deep features closely related to the product performance. Then, considering the correlation between PIs, the multi-task learning framework is introduced to output the subitem ratings and comprehensive product performance rating results of the strip steel online in real-time, where each task represents a subitem of comprehensive performance. Finally, the effectiveness of the method is verified on a real HSMP dataset, and the results show that the accuracy of the proposed method is as high as 94.8%, which is superior to the other comparative methods.