A six-dimensional digital twin model for flexible manufacturing systems in engineering education

Flexible manufacturing system (FMS) teaching in engineering education is difficult due to limited fidelity, inadequate real-time interaction, and poor scalability of typical virtual laboratory platforms. Many existing simulation-based learning environments rely on static or loosely coupled models, which inadequately represent multi-axis machine motion, programmable logic controller (PLC) execution behavior, and industrial communication dynamics found in real manufacturing systems. To address these limitations, this study proposes the Six-Dimensional Digital Twin Framework for Flexible Manufacturing Education (6D-DT-FME), aimed at enabling high-fidelity virtual–physical convergence for instructional applications. The proposed 6D-DT-FME framework integrates six tightly coupled dimensions: geometric modeling, information mapping, kinematic behavior, control logic, industrial communication, and physical state dynamics, forming a comprehensive and behaviorally consistent digital representation of heterogeneous manufacturing equipment. Real-time correspondence between physical systems and the virtual environment is achieved using a time-triggered synchronization algorithm that aligns PLC scan cycles with DT state updates. Cross-dimensional coherence is maintained through a state-space data fusion algorithm that integrates control execution, motion states, communication signals, and physical parameters into a unified system state. Deterministic data exchange among multi-vendor devices is supported through OPC UA and EtherCAT protocols. Scalability in complex virtual workshops is ensured via a quad-tree-based spatial partitioning algorithm within a Unity3D immersive environment, while operational and learning indicators are visualized using a Vue.js and ECharts-based monitoring interface. On an educational FMS testbed, interaction responsiveness is 97.3%, virtual–physical synchronization accuracy is 97.8%, and task execution efficiency is 98.1%, proving the framework for smart manufacturing education is feasible and effective.