KGAssembly: Knowledge graph-driven assembly process generation and evaluation for complex components

The semantic information of process documents for the assembly of complex components plays an important role in the guidance of assembly operations and the feasibility evaluation of process plans. There are many types of semantic elements contained in assembly process documents. The semantic relationship between assembly elements is complex. Additionally, there is a lack of effective modeling method to deal with the implicit associative semantic knowledge that exists among existing assembly process cases. In this case, it is a great challenge to use the professional knowledge in assembly process documents to guide the intelligent decision-making of assembly process planning and the intelligent analysis of assembly process enforceability evaluation. In this paper, a knowledge graph-driven assembly process generation and evaluation method for complex components is proposed. An APKG (assembly process knowledge graph) model is built. A distributed graph embedding-based model SKGCN (sequence knowledge graph convolutional network) is designed to generate assembly process planning. Furthermore, model and knowledge dual-driven evaluation method for assembly sequences is presented. It provides assembly expert knowledge support for the evaluation method of interference detection of assembly sequence based on point cloud assembly feature recognition. Finally, the approach is evaluated by assembling an aero-engine compressor rotor.

Automated summary

This paper proposes a knowledge graph-driven assembly process generation and evaluation method for complex components, including an APKG model and a SKGCN model. Additionally, a model and knowledge dual-driven evaluation method for assembly sequences is presented, providing assembly expert knowledge support for interference detection evaluation in assembly sequences.