Ontology-based Retrieval Augmented Generation (RAG) for GenAI-supported Additive Manufacturing

Published

August 14, 2024

Author(s)

Yeun Park, Paul Witherell, Nowrin Akter Surovi, Hyunbo Cho

Abstract

Conventional data analytics often fail to capture the intricate context of Additive Manufacturing (AM) processes, leading to pointed solutions and suboptimal analytics outcomes. The performance of Generative AI (GenAI) models, such as Large Language Models (LLMs), largely depends on their ability to integrate and contextualize the vast data they are trained on. However, contextualizing is often directly driven by the data consumed, and not necessarily grounded in the fundamental truths. To address this issue, an ontology-based retrieval augmented generation (RAG) approach is proposed to enhance GenAI's capability to generate pertinent prompts and answers. The GenAI recognizes and applies relevant context by leveraging structured ontology, resulting in accurate and insightful interpretations. A use case showcases how the proposed ontology-based RAG framework operates to provide context-aware AM data analytics that promote analytical transparency through fundamental truths when executing AM data analytics.

Proceedings Title

The 35th Annual International Solid Freeform Fabrication Symposium

Conference Dates

August 11-14, 2024

Conference Location

Austin, TX, US

Conference Title

Solid Freeform Fabrication Symposium

Pub Type

Conferences

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Keywords

Additive Manufacturing, Data Analytics, Large Language Models, Retrieval Augmented Generation, Ontology, Knowledge Graph, Prompt Engineering

Citation

Park, Y. , Witherell, P. , Surovi, N. and Cho, H. (2024), Ontology-based Retrieval Augmented Generation (RAG) for GenAI-supported Additive Manufacturing, The 35th Annual International Solid Freeform Fabrication Symposium , Austin, TX, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958349 (Accessed October 9, 2025)