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

• URL: http://arxiv.org/abs/2505.20308v2
• Date: Mon, 28 Jul 2025 05:26:47 GMT
• Title: Large Language Model Powered Decision Support for a Metal Additive Manufacturing Knowledge Graph
• Authors: Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon,
• Abstract summary: Metal additive manufacturing (AM) involves complex interdependencies among processes, materials, feedstock, and post-processing steps.<n>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.<n>A large language model (LLM) interface, guided by a few-shot prompting strategy, enables natural language querying without the need for formal query syntax.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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