Knowledge extraction for additive manufacturing process via named entity recognition with LLMs

This paper proposes a novel NER framework, leveraging the advanced capabilities of Large Language Models (LLMs), to address the limitations of manually defined taxonomy. Our framework integrates the expert knowledge internalized in both academic materials and LLMs through retrieval-augmented generation (RAG) to automatically customize taxonomies for specific manufacturing processes and adopts two distinct strategies of using LLMs — In-Context Learning (ICL) and fine-tuning to complete manufacturing NER tasks with minimal training data. We demonstrate the framework efficiency through its superior ability to define precise taxonomies, identify and classify process-level entities related to the most popular additive manufacturing process fused deposition modeling (FDM) as case study, achieving a high F1 score of 0.9192. • LLM-based NER framework to recognize entities of additive manufacturing process. • Leverage RAG to automatically customize the taxonomy of manufacturing process. • Develop two LLM-based NER approaches based on ICL and fine-tuning respectively.