Abstract
As new AI technologies such as Large Language Models (LLM) quickly evolve, the need for enhancing general-purpose LLMs with physical knowledge to better serve the manufacturing community has been increasingly recognized. This paper presents a method that tailors GPT-3.5 with domain-specific knowledge for intelligent aircraft maintenance. Specifically, aircraft ontology is investigated to curate maintenance logs with encoded component hierarchical structure to fine-tune GPT-3.5. Experimental results demonstrate the effectiveness of the developed method in accurately identifying defective components and providing consistent maintenance action recommendations, outperforming general-purpose GPT-3.5 and GPT-4.0. The method can be adapted to other domains in manufacturing and beyond.
| Original language | English |
|---|---|
| Pages (from-to) | 361-364 |
| Number of pages | 4 |
| Journal | CIRP Annals |
| Volume | 73 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2024 |
Bibliographical note
Publisher Copyright:© 2024 The Author(s)
Funding
The authors would like to thank Dr. Jianjing Zhang from Case Western Reserve University for his valuable contributions. Support from the National Science Foundation under awards CMMI- 2237242 , CNS- 2125460 , and EEC- 2133630 (Engineering Research Center on Hybrid and Autonomous Manufacturing – Moving from Evolution to Revolution) is sincerely appreciated.
| Funders | Funder number |
|---|---|
| National Science Foundation Arctic Social Science Program | CMMI- 2237242, CNS- 2125460, EEC- 2133630 |
Keywords
- Large language models
- Machine learning
- Maintenance
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering