The Knowledge-Based Digital Twin System is a digital twin system developed on the foundation of a knowledge graph, aimed at serving the complex manufacturing process. This system embraces a knowledge-driven modeling approach, aspiring to construct a digital twin model for the manufacturing process, thereby enabling precise description, management, prediction, and optimization of the process. The core of this system lies in the comprehensive knowledge graph that encapsulates all pertinent information about the manufacturing process, facilitating dynamic modeling and iteration through knowledge matching and inference within the knowledge, geometry, and decision model. This approach not only ensures consistency across models but also addresses the challenge of coupling multi-source heterogeneous information, creating a holistic and precise information model. As the manufacturing process deepens and knowledge accumulates, the model's understanding of the process progressively enhances, promoting self-evolution and continuous optimization. The developed knowledge-decision-geometry model acts as the ontological layer within the digital twin framework, laying a foundational conceptual framework for the digital twin of the manufacturing process. Validated on an aero-engine blade production line in a factory, the results demonstrate that the knowledge model, as the core driver, enables continuous self-updating of the geometric model for an accurate depiction of the entire manufacturing process, while the decision model provides deep insights for decision-makers based on knowledge. The system not only effectively controls, predicts, and optimizes the manufacturing process but also continually evolves as the process advances. This research offers a new perspective on the realization of the digital twin for the manufacturing process, providing solid theoretical support with a knowledge-driven approach.

中文翻译：

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基于知识的数字孪生系统：使用知识驱动的方法进行制造流程建模

基于知识的数字孪生系统是在知识图谱基础上开发的数字孪生系统，旨在服务于复杂的制造过程。该系统采用知识驱动的建模方法，旨在构建制造过程的数字孪生模型，从而实现过程的精确描述、管理、预测和优化。该系统的核心在于综合知识图谱，它封装了制造过程的所有相关信息，通过知识、几何和决策模型内的知识匹配和推理，促进动态建模和迭代。这种方法不仅保证了模型之间的一致性，而且还解决了耦合多源异构信息的挑战，创建整体且精确的信息模型。随着制造过程的深入和知识的积累，模型对过程的理解逐步增强，促进自我进化和不断优化。开发的知识-决策-几何模型充当数字孪生框架内的本体层，为制造过程的数字孪生奠定了基础概念框架。在某工厂航空发动机叶片生产线上进行验证，结果表明，知识模型作为核心驱动，能够不断自我更新几何模型，准确描述整个制造过程，而决策模型则提供为决策者提供基于知识的深刻见解。该系统不仅有效地控制、预测和优化制造过程，而且随着过程的进步而不断发展。该研究为制造过程中数字孪生的实现提供了新的视角，以知识驱动的方法提供了坚实的理论支持。