Association rule mining for aircraft assembly process information based on fine-tuned LLM

Abstract The aircraft final assembly is a complex system, encompassing various aspects and multidimensional production factors. These numerous factors are interconnected, significantly impacting the efficiency of the final assembly process. To investigate the interrelationships among various production factors, this study introduces a specialized fine-tuning large language model for aircraft final assembly, termed Aircraft Final Assembly ChatGLM (AFA-ChatGLM). This model is designed to automatically extract essential information regarding key production factors from process documentation. Furthermore, the FP-Growth algorithm is employed to uncover association rules between these production factors and the various stages of the final assembly. Experimental results indicate that our method demonstrates outstanding performance in the aircraft final assembly domain. Specifically, for the assembly process documents of the C919 large passenger aircraft, our proposed model achieved a Precision of 82.7%, Recall of 89.1%, and F1 score of 85.4%, representing a substantial improvement over traditional word segmentation methods. leveraging the superior performance of the model, we utilized association rule mining techniques to construct 44,851 high-confidence association rules for the final assembly line of the C919, laying a foundation for subsequent optimization of the production line.