Asset Administration Shell-based Automatic Generation of Digital Twin Models for Discrete Event Simulation

A digital twin is a virtualized representation of a physical asset that mirrors and synchronizes real-world behavior within a virtual environment. Among its core functionalities, simulation plays a critical role in enabling real-time prediction and analysis during system operation. However, the practical implementation of digital twins remains challenging due to difficulty of real-time model generation, the high complexity of modeling processes, and the lack of standardized frameworks. To overcome these limitations, this study proposes an automated method for generating digital twin and simulation models based on the Asset Administration Shell(AAS), an international standard for interoperability in Industry 4.0 environments. The proposed approach utilizes AAS to define structured simulation input data, which are subsequently mapped onto predefined simulation templates to automate the model generation process. The objective of this study is to develop a technology that enhances modeling efficiency and ensures real-time responsiveness through an AAS-based automatic generation method, thereby enabling rapid adaptation to production changes and supporting real-time, simulation-based decision-making. This study illustrates the proposed framework through a case study.