Facilitating Design for Additive Manufacturing with KG-based Retrieval-Augmented Generation

Additive manufacturing (AM), or 3D printing, enables the production of complex geometries and highly customized components. However, design for AM (DfAM) requires specialised and comprehensive knowledge of process constraints, material behaviours, and performance parameters. While knowledge graphs (KGs) have been utilized to organize and integrate DfAM knowledge, they do not understand natural language and have limited reasoning capabilities, which make them less accessible to non-experts and ineffective in handling complex and context-dependent design queries. Large language models (LLMs) offer powerful language processing and generalizability but suffer from hallucinations when handling specialized domains. To address these limitations, this study proposes a KG-based retrieval-augmented generation (RAG) approach to develop a domain-specific question-answering (Q&A) in DfAM. By integrating structured knowledge from a DfAM KG with the strengths of LLMs, the proposed approach improves response accuracy and relevance. Comparative experiments evaluated LLMs with non-RAG and KG-based RAG using generic and domain-specific metrics. Results demonstrated that KG-based RAG enhances information retrieval and response quality, reduces hallucinations, and ensures alignment with domain knowledge.