KGESM: A knowledge graph embedding-based similarity matching model for intelligent assembly process generation

Abstract

The assembly process knowledge graph is an important carrier for assembly process knowledge management and reuse in manufacturing enterprises. Its important application scenario is to generate assembly process. However, the primary method is to perform simple retrieval on the knowledge graph using the graph database tool. This method can only retrieve the identical assembly process due to the lack of deep semantics for process knowledge, which restricts the flexibility of assembly process generation. Since there may be different representations and descriptions of identical process knowledge, it is necessary to mine deep semantic information to achieve process generation. To address these challenges, we propose a knowledge graph embedding-based similarity matching model for intelligent assembly process generation. First, we build a knowledge graph embedding-based similarity matching model called KGESM. Then, we construct a dataset consisting of a series of assembly process knowledge pairs extracted from actual electronic equipment manufacturing documents. Finally, the trained model is used to generate assembly processes according to new manufacturing needs. We conduct comprehensive experiments on the electronic equipment assembly process knowledge graph, where the mean square error of similarity matching achieves $1.200\times 10^{-3}$. Unlike traditional knowledge graph retrieval, similarity matching based on assembly process knowledge graph embedding has the advantage of fusing the features of assembly process nodes and assembly relations. Furthermore, examples of electronic equipment assembly processes are generated, and the highest similarity score of the generated assembly processes is 0.939, which proves the feasibility of our method in the equipment manufacturing field.