Self-configuration is the preparation required to facilitate smart-manufacturing (SM) with the inputs derived without user intervention for engineering applications. Thus, it is vital for achieving the highest maturity level of SM technologies. In context, digital twin (DT) is an advanced virtual factory with simulation as its core technical functionality. However, the requirement of several inputs limits the implementation of DT on a physical asset without user intervention. Moreover, surpassing this limitation requires extraction methods for deriving the necessary inputs for DT application. Therefore, this study proposes information fusion and systematic logic library (SLL)-generation methods to facilitate the self-configuration of an autonomous DT. The information fusion aggregates and extracts the information elements required for DT application from heterogeneous information sources. In addition, the SLL generation method created the SLL required for reflecting the functional units of agents within the physical asset. Both methods were proposed from available SM standards such as ISA-95, automation markup language, and OPC unified architecture. Furthermore, an autonomous DT-supporting framework was designed by analyzing the relationship between asset description and SM standards, which facilitated the artificial intelligence-based extraction of the asset description object and SLL. Additionally, the core functional engines within this framework were designed using machine learning and process-mining techniques. Consequently, the proposed methods reduced the input pre-processing time required for constructing and synchronizing an autonomous DT to aid the application of autonomous DT on the physical asset without user intervention.

自配置是促进智能制造 (SM) 所需的准备，其输入无需用户干预即可用于工程应用。因此，实现 SM 技术的最高成熟度是至关重要的。在上下文中，数字孪生 (DT) 是一种先进的虚拟工厂，其核心技术功能是模拟。然而，多个输入的要求限制了 DT 在没有用户干预的情况下在物理资产上的实施。此外，超越这一限制需要提取方法来获得 DT 应用程序的必要输入。因此，本研究提出了信息融合和系统逻辑库（SLL）生成方法，以促进自主 DT 的自配置。信息融合从异构信息源中聚合和提取DT应用所需的信息元素。此外，SLL 生成方法创建了反映实物资产内代理功能单元所需的 SLL。这两种方法都是根据现有的 SM 标准提出的，例如 ISA-95、自动化标记语言和 OPC 统一架构。此外，通过分析资产描述和SM标准之间的关系，设计了一个自治的DT支持框架，促进了基于人工智能的资产描述对象和SLL的提取。此外，该框架内的核心功能引擎是使用机器学习和流程挖掘技术设计的。最后，