Research on production line simulation method based on digital twin

Abstract

Addressing the challenge of insufficient behavioral modeling accuracy in digital twin-driven production line simulation, this study targets the inherent limitations of "state explosion" and "behavioral black box" in traditional Finite State Machines when describing complex, concurrent equipment beha)viors. An innovative simulation method is proposed, centering on an Improved Finite State Machine (IFSM) that formally incorporates a set of intermediate states to explicitly characterize component-level collaborative workflows and key transition steps. This is integrated within a three-tier modeling architecture to enable modular encapsulation and reuse of behavioral logic. Experimental validation through a stacker case study demonstrates that the implemented prototype system effectively simulates normal, disturbed, and fault conditions. Compared to traditional FSMs, the proposed method reduces model transition rule complexity and achieves component-level fault localization accuracy while maintaining real-time performance. This study proves the method's feasibility and superiority in enhancing the correctness, stability, and maintainability of production line simulations, providing a viable technical pathway for constructing high-fidelity and high-reliability digital twin systems.