Large Language Model Powered Decision Support for a Metal Additive Manufacturing Knowledge Graph
This provides accessible and explainable decision support for engineers in manufacturing, though it is incremental as it combines existing knowledge graph and LLM techniques in a domain-specific application.
The paper tackled the problem of fragmented knowledge in metal additive manufacturing by developing a knowledge graph covering 53 metals and alloys across various categories and processes, and introduced an LLM interface for natural language querying, resulting in an interactive system that supports tasks like compatibility evaluation and design guidance without requiring expert queries.
Metal additive manufacturing (AM) involves complex interdependencies among processes, materials, feedstock, and post-processing steps. However, the underlying relationships and domain knowledge remain fragmented across literature and static databases that often require expert-level queries, limiting their applicability in design and planning. To address these limitations, we develop a novel and structured knowledge graph (KG), representing 53 distinct metals and alloys across seven material categories, nine AM processes, four feedstock types, and corresponding post-processing requirements. A large language model (LLM) interface, guided by a few-shot prompting strategy, enables natural language querying without the need for formal query syntax. The system supports a range of tasks, including compatibility evaluation, constraint-based filtering, and design for AM (DfAM) guidance. User queries in natural language are normalized, translated into Cypher, and executed on the KG, with results returned in a structured format. This work introduces the first interactive system that connects a domain-specific metal AM KG with an LLM interface, delivering accessible and explainable decision support for engineers and promoting human-centered tools in manufacturing knowledge systems.