About this Abstract |
Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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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 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 the capability of LLMs to generate pertinent prompts and answers. By leveraging structured ontology, the LLM recognizes and applies relevant context, resulting in accurate and insightful interpretations. The approach includes three main components: AMDataAnalytics Ontology, AMPrompt Generator, and AMContext Extractor. A use case showcases how the components work together 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 |