Aircraft assembly process planning based on knowledge graph constructed by integrating LLMs and SLMs

In commercial aircraft manufacturing, process planning serves as a crucial bridge between design and production, ensuring the accurate realization of design concepts and significantly improving manufacturing efficiency and product quality. With the development of knowledge graph technologies, significant progress has been made in using historical process documentation for commercial aircraft manufacturing process planning. However, traditional deep learning-based methods for constructing knowledge graph heavily rely on manual object selection and label assignment, making the process highly time-consuming. Additionally, the methods often face challenges in the field of process planning, including low domain-specific terminology recognition rates and incomplete entity extraction. To tackle these challenges, this paper introduces a hybrid approach that integrates large and small language models to construct an aircraft process planning knowledge graph. Initially, clustering-based multi-agent approach is employed to pre-annotate the process planning dataset, with domain experts re-annotate the defect data to create a high-quality process planning dataset. Subsequently, a knowledge extraction framework for aircraft process planning, KE-LSM, was constructed using the small language model trained on this dataset, together with the LLM. Experimental results show that KE-LSM outperforms existing named entity recognition models. Finally, KE-LSM is applied in a commercial aircraft manufacturing company, accompanied by the development of a prototype system designed to facilitate intelligent process planning. It is hoped that the research can provide valuable insights and support for the application of LLM-based solutions in the field of aircraft manufacturing.