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Knowledge Base Repository

In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.

Integrating large language models for improved failure mode and effects analysis (FMEA): a framework and case study

Ibtissam El Hassani (1,2), Tawfik Masrour (1,2), Nouhan Kourouma (1), Damien Motte (3), Jože Tavčar (3)

Type:
Papers
Proceedings of the DESIGN2024 18th International Design Conference
Year:
2024
Editor:
Mario Štorga, Stanko Škec, Tomislav Martinec, Dorian Marjanović, Neven Pavković, Marija Majda Škec
Author:
Ibtissam El Hassani (1,2), Tawfik Masrour (1,2), Nouhan Kourouma (1), Damien Motte (3), Jože Tavčar (3)
Series:
DESIGN
Institution:
1: Moulay Ismail University, Morocco; 2: University of Quebec at Rimouski, Canada; 3: Lund University, Sweden
Section:
Artificial Intelligence and Data-Driven Design
Page(s):
2019-2028
DOI number:
10.1017/pds.2024.204
ISSN:
2732-527X (Online)
Abstract:
The manual execution of failure mode and effects analysis (FMEA) is time-consuming and error-prone. This article presents an approach in which large language models (LLMs) are integrated into FMEA. LLMs improve and accelerate FMEA with human in the loop. The discussion looks at software tools for FMEA and emphasizes that the tools must be tailored to the needs of the company. Our framework combines data collection, pre-processing and reliability assessment to automate FMEA. A case study validates this framework and demonstrates its efficiency and accuracy compared to manual FMEA.
Keywords:
failure mode and effect analysis (FMEA), large language model (LLM), generative AI, product quality, knowledge management
18th International Design Conference (DESIGN 2024)
Conferences

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