An automatic machining process decision-making system based on knowledge graph

ABSTRACT

Automatic process decision-making is a key module in intelligent process design(IPD), which determines the intelligence degree of IPD and affects the quality of product design. The traditional process decision-making method fails to solve the problem of knowledge expression, especially the integration of enterprise manufacturing resources and process knowledge. What’s more, heterogeneous knowledge also leads to the application of traditional knowledge mainly in keyword retrieval. So the process reasoning is mainly applied to the feature level, but the reasoning ability for the part level is weak. To overcome the above problems, the Knowledge Graph(KG) is introduced into the automatic machining process decision-making system. Firstly, a three-level information model is built to reorganize part information, process knowledge, and equipment resources based on KG. Secondly, the process reasoning framework based on KG is established, which is composed of process knowledge graph(PKG) information and process reasoning algorithm. Thirdly, to integrate process reasoning based on PKG, a hybrid reasoning algorithm based on semantic analysis(SA) and attributes weighting(AW) is built, which solved the problem of heterogeneity among process knowledge when making decisions. Finally, a prototype system was developed, and the aero-engine cone gear axis was tested to verify the effectiveness of the proposed system.

KEYWORDS:

• Knowledge graph
• process reasoning
• automatic decision-making
• knowledge base

Acknowledgments

We would like to thank the reviewers and the editor for their constructive comments and suggestions to this paper. We also acknowledge the Youth Science Foundation of National Natural Science Foundation (No.51705438), Sichuan science and technology project (No.2018JY0366), Young Science and Technology Innovation Team of SWPU(No. 2019CXTD02), Independent Innovation Special Fund Project, AECC(No.ZZCX-2017-039) and Chengdu International Science and Technology Cooperation Project (No:2020-GH02-00040-HZ).

Additional information

Funding

This work was supported by the Youth Science Foundation of National Natural Science Foundation [No.51705438]; Independent Innovation Special Fund Project [No.ZZCX-2017-039]; Sichuan science and technology project [No.2018JY0366]; Chengdu International Science and Technology Cooperation Project [No:2020-GH02-00040-HZ]; Young Science and Technology Innovation Team of SWPU [No.2019CXTD02].