Sparse attention-driven quality prediction for production process optimization in digital twins

Abstract

In the process industry, long-term and efficient optimization of production lines requires real-time monitoring and analysis of operational states to fine-tune production line parameters. However, complexity in operational logic and intricate coupling of production process parameters make it difficult to develop an accurate mathematical model for the entire process, thus hindering the deployment of efficient optimization mechanisms. In view of these difficulties, we propose to deploy a digital twin (DT) of the production line by encoding its operational logic in a data-driven approach. By iteratively mapping the real-world data reflecting equipment operation status and product quality indicators in the DT, we adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks (NNs). This model enables the data-driven state evolution of the DT. The DT takes a role of aggregating the information of actual operating conditions and the results of quality-sensitive analysis, which facilitates the optimization of process production with virtual-reality evolution. Leveraging the DT as an information-flow carrier, we extract temporal features from key process indicators and establish a production process quality prediction model based on the proposed deep NN. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed DT-based production process optimization method fosters seamless integration between virtual and real production lines. This integration achieves an average operating status prediction accuracy of over 98% and a product quality acceptance rate of over 96%.