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Enabling Grounded Answers Through Knowledge Graphs and Retrieval Augmented Generation School of Mechanical, Aerospace, and Manufacturing Engineeri
- Format:
- Book
- Conference/Event
- Author/Creator:
- Hoang, Danny, author.
- Conference Name:
- 2025 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium (2025-08-12 : Novi, Michigan, United States)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2025
- Summary:
- In modern defense manufacturing, achieving technological superiority hinges on both rapid decision-making and unparalleled precision engineering. Advanced machining systems, such as 5-axis CNC machines, play a pivotal role by enabling the production of intricate, free-form geometries with micron-level accuracy. However, these advances often necessitate deep domain expertise for optimal tool selection and machining parameter configuration. This paper introduces GraphLLM, a model-agnostic approach that integrates structured knowledge graphs with large language models (LLMs) to enhance the accuracy and reliability of technical responses. By automatically extracting domain-specific entities and relationships from documents, GraphLLM mitigates LLM hallucinations and improves performance, especially in technically challenging or out-of-distribution queries. Experimental evaluations across various LLaMA models demonstrate significant uplifts of 25%, highlighting the framework's potential to provide grounded answers for decision-making in advanced manufacturing
- Notes:
- Vendor supplied data
- Publisher Number:
- 2025-01-0488
- Access Restriction:
- Restricted for use by site license
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