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.
@article{arxiv.2505.20308,
title = {Large Language Model Powered Decision Support for a Metal Additive Manufacturing Knowledge Graph},
author = {Muhammad Tayyab Khan and Lequn Chen and Wenhe Feng and Seung Ki Moon},
journal= {arXiv preprint arXiv:2505.20308},
year = {2025}
}
Comments
The paper has been accepted at 11th International Conference of Asian Society for Precision Engineering and Nanotechnology