Intelligent process design is the core of process design in the digital twin environment

and part process knowledge modeling is a prerequisite for achieving intelligent process design based on digital twins. To address the issues of low structured and difficult to reuse machining process data for complex thin-walled parts in the aerospace field

a onstruction and quality evaluation method for a typical knowledge graph of machining process for complex thin-walled parts is proposed. Firstly

the composition and structure of machining process knowledge are analyzed. Secondly

the visualization of process knowledge was realized through ontology modeling

knowledge extraction

knowledge storage

and other related technologies

and the knowledge retrieval of machining process was realized based on Neo4j database. Finally

the analytic hierarchy process is used to evaluate the constructed knowledge map

and the machining process knowledge of frame and segment parts is taken as the verification object

the comprehensive accuracy of the sub-map is 92.28%. The experimental results show that the process knowledge modeling method based on knowledge map is feasible

which can help to realize the effective organization and reuse of process knowledge

and lay the foundation for digital twin intelligent process design.