A method for the spatiotemporal correlation prediction of the quality of multiple operational processes based on S-GGRU

Predicting quality indicators is essential in process production, process production often involves multiple processes. However, modeling the prediction model for quality indicators faces challenges due to frequent fluctuations in raw material composition and the control system's automatic adjustment of process parameters. Additionally, due to the mutual influence between processes, the problem of predicting quality indicators in process production is a systematic prediction problem. In order to deal with the challenges, firstly, the physical control relationships are transformed into graph data, which is input into GNN to calculate the spatial characteristics of process parameters on quality indicators under physical information constraints. GRU captures the temporal features of quality indicator data. Based on above study, a combination network GGRU integrating quality indicators' spatial and temporal characteristics was proposed. Finally, each GGRU is then connected based on the material flow between each process, and path information between processes is converted to the calculation flow between GGRU models to form the S-GGRU prediction model. Experimental results show that the proposed S-GGRU model, a multi-process prediction model based on GRU and GNN, outperforms the recurrent neural network model and the graphical neural network model in solving the quality prediction problem for multi-process and multi-metrics in-process production. Therefore, both the time-series information of quality metrics and the influencing factors among variables should be considered to achieve better predictions, and the S-GGRU performance better in predicting quality indicators for a 5-process process line.