Construction and Reasoning Method of Knowledge Graph for Aircraft Skin Spraying Process

To address the heavy reliance on experiential knowledge, fragmented multi-source information, and limited intelligence in decision-making for aircraft skin spraying processes, this paper proposes a knowledge reasoning method based on a knowledge graph. The authors construct a knowledge graph that integrates multi-structure ontology and physical rule constraints. This graph systematically organizes and manages multi-dimensional knowledge, including painting object attributes, paint performance indicators, and spraying parameters. On this basis, a three-stage reasoning mechanism with multi-granularity semantic understanding, knowledge enhancement, feature fusion, and multi-constraint intelligent matching (MKM) is designed. The model can perform semantic analysis of the user’s fuzzy query, implicit knowledge completion, and dynamic subgraph matching, so as to give the aircraft skin spraying process plan that meets the constraints of safety, compatibility, and feasibility. The experimental results show that the proposed method is superior to the traditional case-based reasoning method, graph convolutional network method, and knowledge graph embedding method in the key evaluation indices of Hit@1, Hit@3, and MRR in the knowledge reasoning task of aircraft skin spraying process. It also has good robustness and promotion value when data are scarce and parameters are uncertain. This study provides a feasible method of intelligent management and dynamic decision-making in terms of aircraft skin spraying process knowledge, and may be applied to other manufacturing fields.