Knowledge Graph-Driven Manufacturing Resources Recommendation Method for Ship Pipe Manufacturing Workshop

In the context of the digitized and intellectualized transformation of ship pipe manufacturing enterprises, how to transform the massive multi-source heterogeneous data into knowledge and realize the reuse of manufacturing knowledge and experience in the ship pipe manufacturing workshop are the key to optimizing the allocation of workshop manufacturing resources. In order to solve the aforementioned issues, a knowledge graph-driven manufacturing resource recommendation method for ship pipe manufacturing workshops is proposed. Firstly, the correlation between multi-sources heterogeneous manufacturing data (device resources, manufacturing process of pipe, manufacturing orders) is analyzed and integrated. Then, a knowledge graph of manufacturing resources for the ship pipe manufacturing workshop is constructed. On this basis, a manufacturing resource recommendation method based on the Knowledge Graph Convolution Networks is proposed to recommend the device for orders in the ship pipe manufacturing workshop. Finally, a case study is implemented to verify the feasibility and effectiveness of the proposed method.