Digital twin system for manufacturing processes based on a multi-layer knowledge graph model
Chang Su, Xin Tang, Qi Jiang, Yong Han, Tao Wang, Dongsheng Jiang
Abstract
Digital twin technology in the manufacturing process faces challenges like integrating diverse data sources and managing real-time data flow. To address this, we propose a novel three-layer knowledge graph architecture to enhance digital twin modeling for manufacturing processes. This architecture consists of a concept layer that structures key information into a knowledge network, a model layer that aligns digital and physical parameters, and a decision layer that leverages model and real-time data for decision support. Validated in aero-engine blade production, this system integrates multi-source data, enhances predictive analysis and anomaly detection, and supports process control and quality management. Over a 5-month validation period, the maximum contour error precision of the blades improved from 0.073 mm to 0.062 mm, and the product qualification rate increased from 81.3% to 85.2%. This demonstrates the system's robust capability for advancing digital twin utilization in manufacturing, highlighting its potential for future improvements.