AI-driven FMEA: integration of large language models for faster and more accurate risk analysis
Failure mode and effects analysis (FMEA) is a critical but labor-intensive process in product development that aims to identify and mitigate potential failure modes to ensure product quality and reliability. In this paper, a novel framework to improve the FMEA process by integrating generative artif...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2025-01-01
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| Series: | Design Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2053470125000071/type/journal_article |
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