Multiple Orders Remaining Completion Time Prediction Based on Digital Twin and Spatiotemporal Multi-Graph Network Cascade Algorithm

In discrete manufacturing systems, the concurrent production of customized orders increases the complexity of multi-order remaining completion time (MORCT) prediction. Most existing methods rely on empirical estimates of production capacity, ignoring production planning constraints and failing to incorporate real-time manufacturing performance, resulting in limited prediction accuracy. To address this, a DT-based MORCT prediction framework is proposed. It constructs spatiotemporal datasets of production tasks and performance from DT and introduces a spatiotemporal multi-graph cascade algorithm (STMG) for prediction. The algorithm first embeds data into two types of graph models, which contain manufacturing system information and order-task scheduling information, extracts real-time performance features across multiple manufacturing units (MUs), and applies heterogeneous graph attention to model the embedded features of order nodes in the order-task-MU graph for final MORCT prediction. The experimental results show that the STMG model can accurately predict MORCT and is suitable for parallel production scenarios with multiple orders. It outperforms existing methods in terms of loss and adaptability.