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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
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86-571-87239571
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No.9 Gaoguannong,Daxue Road,Hangzhou,China
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meem_contribute@163.com
Abstract: Aiming at the problem that the machining data of aerospace complex thin-walled parts is low structured and difficult to be reused, a knowledge graph was introduced into the field of machining process design, and an approach of constructing a knowledge graph for machining typical complex thin-walled parts in aerospace application was proposed. First, the process knowledge hierarchy was defined from top to bottom, and the schema layer was constructed by ontology modeling. Second, the methods of knowledge extraction, knowledge fusion and ontology relationship establishment were used to build the data layer from the bottom to the top. The mapping between schema layer and data layer was completed by using Neo4j graph database, which realized the visual representation and fast retrieval of process knowledge. Then, on the basis of the completed process knowledge graph, the process route recommendation was realized by combining the similarity of part attributes and feature topological relationship. Finally, the visualization system of knowledge graph for machining process of complex thin-walled parts was built, and the function of machining knowledge retrieval and machining route recommendation was demonstrated by taking frame segment parts as an example. The research results show that the process route recommendation model based on knowledge graph and similarity calculation is tested for 500 times, and 94.7% of the recommended list has the process route matching with the target part. It is proved that the construction method of the process knowledge graph is feasible, which can play an auxiliary role in process design decision-making, and can effectively improve the efficiency of process query and design.
Key words: machining process design; processing technology of complex thinwall parts; knowledge fusion; process route recommendation; knowledge extraction; visualization system
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