On Sequential-Pattern-Based Intelligent Compilation of Aircraft Assembly Process

The aviation industry which aircraft manufacturing dominates is an important sector to measure a nation’s air power. The application of new technologies to optimize the process, increase the speed and upgrade the quality of airplane assembly, a crucial link in its manufacturing, is an effective method to improve the capacity. The combination of traditional processes and emerging technologies greatly improves its efficiency, reduces its labor costs and promotes the innovation and upgrade of traditional technologies. For aircraft manufacturers, the Assembly Outline (AO) refers to an essential document used for the guidance of the assembly process. Low efficiency and under-structuration of compilation, which is also difficult to upgrade, put a limit on the application of information and digitization technologies and has an impact on the overall airplane production cycle, due to present manual work in quantity and imbalance of informationization. Via sequential-pattern-based data mining introduced in this paper, patterns of AO processes are dug up to form a set based on which it is possible to quickly make a recommendation of the optimal process for the compiler, effectively reducing the duration of compilation and modification and improving the efficiency.