Large Language Model Powered Decision Support for a Metal Additive Manufacturing Knowledge Graph

TL;DR

This paper presents a novel decision support system that integrates a comprehensive metal additive manufacturing knowledge graph with a large language model interface, enabling natural language queries for design, compatibility, and process planning.

Contribution

It introduces the first interactive system combining a domain-specific metal AM knowledge graph with an LLM interface for accessible decision support in manufacturing.

Findings

Supports natural language querying of complex AM knowledge.

Enables compatibility evaluation and design guidance.

Facilitates human-centered decision making in metal additive manufacturing.

Abstract

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.