| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Ontology-based Retrieval Augmented Generation (RAG) for GenAI-Supported Additive Manufacturing |
| Author(s) |
Yeun Park, Paul Witherell, Nowrin Akter Surovi, Hyunbo Cho |
| On-Site Speaker (Planned) |
Yeun Park |
| Abstract Scope |
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 Inclusion? |
Definite: Post-meeting proceedings |