Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

Knowledge Graph Based Method for Tracing Quality of Aerospace Products

Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.