Abstract
Mass personalization is an achievable manufacturing paradigm, which requires flexible and responsible manufacturing operations in response to dynamic batch sizes of personalized products. A Self-Organizing Manufacturing Network (SOMN) has been proposed to achieve mass personalization. A crucial aspect of SOMN is adaptive manufacturing control, and the Knowledge Graph, a powerful tool, has been recognized as a promising solution to enhance manufacturing intelligence. However, the current Knowledge Graph research mainly focuses on the modeling and ontology definition of the manufacturing environment, but neglects the interaction between manufacturing resources, the dynamic features of the manufacturing environment, and the application of the Knowledge Graph towards adaptive manufacturing control. Therefore, this paper proposes a Knowledge Graph-based semantic representation for adaptive manufacturing control under dynamic manufacturing environments. The proposed approach develops the Knowledge Graph based on historical and real-time scheduling data. Based on the established Knowledge Graph, Multi-Agent Reinforcement Learning has been introduced as an illustrative example of achieving adaptive scheduling control.
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