A Knowledge-Driven Reconfigurable Modeling Method for Digital Twin Flexible Production Lines

In order to meet the demands of intelligent manufacturing and digital transformation, flexible reconfigurable production lines have gradually become a key technological approach for complex assembly production. This paper proposes a knowledge-driven modeling method for flexible reconfigurable production lines by integrating knowledge graph and digital twin technology. Based on a multilevel modeling approach, the method constructs knowledge graph models for 4 Layers. Through the abstraction and multilevel progressive modeling of various production factors, this method enables dynamic modeling and precise reconfiguration of complex assembly production lines. The paper uses the final assembly production line as a case study to validate the flexible adaptability and reconfiguration capabilities of different assembly materials, tooling equipment, and production line layouts under multi-variety and small-batch production conditions. This approach provides strong modeling support for flexible production in intelligent manufacturing environments.