Onto-SAGCN: Ontology modeling and spatial attention-based graph convolution networks for aircraft assembly quality prediction

Abstract

Aircraft assembly is an essential stage in the aircraft manufacturing industry, and the increasing complexity of aircraft functionality has put higher requirements for assembly quality. Various deep learning methods based on image or structural data have been used to predict assembly quality. However, these methods focus more on the structural relationships and interactions between assembled products and are difficult to adapt to scenarios where products and equipment may change. This paper proposes a graph convolutional neural network based on ontology modeling and spatial attention mechanism (Onto-SAGCN) for assembly quality analysis and prediction. Specifically, formal representations of assembly equipment, products, and assembly processes are established using ontologies, enabling the unified modeling of spatial and temporal relationships between entities and resolving the issues of data representation inconsistency. Then, the ontologies are transformed into undirected graphs, where nodes represent entities, node attributes represent feature data, and edges represent relationships or constraints between nodes. Designed to process data from the assembly process, the Onto-SAGCN model is subsequently applied to the assembly of aircraft moving wings. It predicts the diameters of holes by gathering operational data from equipment during the assembly and benchmarks these predictions against other established methods. Experimental outcomes affirm the method's elevated accuracy and dependability in forecasting assembly quality.