Advancing Capability Matching in Manufacturing Reconfiguration with Large Language Models

Mo, Fan; Chaplin, Jack C.; Sanderson, David; Ratchev, Svetan

doi:10.1007/978-3-031-74485-3_24

Advancing Capability Matching in Manufacturing Reconfiguration with Large Language Models

Mo, Fan; Chaplin, Jack C.; Sanderson, David; Ratchev, Svetan

Authors

Fan Mo

Dr JACK CHAPLIN Jack.Chaplin@nottingham.ac.uk
ASSISTANT PROFESSOR

Dr David Sanderson DAVID.SANDERSON@NOTTINGHAM.AC.UK
SENIOR RESEARCH FELLOW

Professor SVETAN RATCHEV svetan.ratchev@nottingham.ac.uk
CRIPPS PROFESSOR OF PRODUCTION ENGINEERING & HEAD OF RESEARCH DIVISION

Abstract

This paper introduces an approach that integrates Natural Language Processing (NLP) and knowledge graphs with Reconfigurable Manufacturing Systems (RMS) to enhance flexibility and adaptability. We utilize a chatbot interface powered by GPT-4 and a structured knowledge base to simplify the complexities of manufacturing reconfiguration. This system not only boosts reconfiguration efficiency but also broadens accessibility to advanced manufacturing technologies. We demonstrate our methodology through an application in capability matching, showcasing how it facilitates the identification of assets for new product requirements. Our results indicate that this integrated solution offers a scalable and user-friendly approach to overcoming adaptability challenges in modern manufacturing environments.

Citation

Mo, F., Chaplin, J. C., Sanderson, D., & Ratchev, S. (2024, June). Advancing Capability Matching in Manufacturing Reconfiguration with Large Language Models. Presented at International Conference on Flexible Automation and Intelligent Manufacturing, Taichung, Taiwan

Presentation Conference Type	Conference Paper (published)
Conference Name	International Conference on Flexible Automation and Intelligent Manufacturing
Start Date	Jun 23, 2024
End Date	Jun 26, 2024
Online Publication Date	Dec 13, 2024
Publication Date	Jan 1, 2024
Deposit Date	Mar 21, 2025
Publicly Available Date	Mar 25, 2025
Print ISSN	2195-4356
Electronic ISSN	2195-4364
Peer Reviewed	Peer Reviewed
Pages	215-222
Series Title	Lecture Notes in Mechanical Engineering
Series ISSN	2195-4356
Book Title	Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order: Proceedings of FAIM 2024, June 23–26, 2024, Taichung, Taiwan, Volume 2
ISBN	9783031744846
DOI	https://doi.org/10.1007/978-3-031-74485-3_24
Public URL	https://nottingham-repository.worktribe.com/output/44824843
Publisher URL	https://link.springer.com/chapter/10.1007/978-3-031-74485-3_24