A knowledge graph-based operational data representation method of digital twin shop-floor towards smart manufacturing

As a means to achieve the integration of cyber-physical systems, the digital twin shop-floor (DTS) provides extensive data about shop-floor operations through dynamic interaction. Operational data collected during the manufacturing process is characterized by its multi-dimensional, heterogeneous, and time-series nature. To fully leverage this information and facilitate informed decision-making, it is essential to develop an effective method for representing the data in a unified and integrated manner. This paper proposes a knowledge graph-based operational data representation method for the DTS. The knowledge graph captures the entities and relationships related to the DTS, providing semantic integration of production line and process information. Operational data from various information systems are analyzed and managed comprehensively, utilizing the knowledge graph-based method to establish a unified, quantified, formal, and logical representation of the DTS operational data. This multi-dimensional knowledge structure encompasses manufacturing resource information, manufacturing process information, manufacturing task information, and manufacturing execution information. The proposed method is demonstrated through an example of a refrigerator manufacturing shopfloor. The resulting data representation facilitates DTS operational data analysis and cognitive decision-making promoting smart production during the manufacturing process. Additionally, this method explores the organization, acquisition, and reuse of knowledge in the DTS, along with the self-learning and self-cognitive capabilities of smart manufacturing systems.