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

Abstract

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.

Digital Twin Framework for Built Environment: A Review of Key Enablers

“…Quality management in assembly process 2022 [117] Information management 2022 [118] Geometrical variation prediction 2022 [119] Multi-input loads monitoring 2022 [120] AR-based Learning Speed and Task Performance. 2021 [121] Multi-dimensional machining process data 2021 [122] Condition Based Maintenance 2021 [123] Modelling simulations 2020 [103] Industry and Manufacturing Model-driven engineering 2021 [124] DTs and CPS 2019 [125] Production control optimization 2019 [126] Real-time feedback from virtual to real space 2021 [51] Planning and commissioning optimization 2021 [127] Smart Manufacturing System early detection 2021 [108] Energy DT-based energy optimization solutions 2019 [128] Distributed real-time process data 2017 [129] Performance predictions 2020 [130] Continuous tracking and simulation 2020 [131] Hybrid modelling performance monitoring method 2020 [114]…”

Digital Twins for Built Environment. A Review on Key Enablers

“…This virtual modelʹs data is combined with the data from the physical models to produce highly accurate predictions [112]. Tuegel et al [116] suggest that using digital platforms can be helpful in predicting the structural life reengineering process of an aircraft. One of the top design systems and components manufacturers for aerospace and defense organizations, Test-Fuchs, has successfully implemented a dedicated digital twin approach for test equipment [113].…”