A large language model-enabled machining process knowledge graph construction method for intelligent process planning

Abstract

As a pivotal step in translating design into production, process planning significantly influences product quality, cost, production efficiency, and market competitiveness. The process knowledge base, a fundamental element of process planning, determines the intelligence level of product manufacturing. Methods that construct process knowledge bases using Knowledge Graphs (KGs) have increasingly become critical technologies for supporting intelligent process planning. However, traditional deep learning-based named entity recognition methods for constructing KGs require extensive manual effort in domain-specific data annotation, resulting in inefficiencies, prolonged construction cycles, and high costs. To address these challenges, this paper introduces a Large Language Model-enabled method for constructing Machining Process KGs (LLM-MPKG). Initially, Large Language Models (LLMs) are employed to pre-annotate machining process text datasets. A verifier is then developed to assess and filter the pre-annotated datasets, with domain experts re-annotating deficient data to create a high-quality annotated machining process dataset. Subsequently, using this dataset and a fine-tuned LLM, a machining process knowledge extraction model, MPKE-GPT, is constructed. MPKE-GPT is then applied to extract knowledge from process planning case data for 50 parts within an enterprise, leading to the creation of the MPKG. A prototype system was also developed to support intelligent process planning. Compared to traditional deep learning methods, the proposed method reduces construction time by 48.58%, lowers costs by 46.44%, and enhances performance by 1.96%.