Intelligent configuration management in modular production systems: Integrating operational semantics with knowledge graphs

Rehman, Hamood Ur; Mo, Fan; Chaplin, Jack C.; Zarzycki, Leszek; Jones, Mark; Maffei, Antonio; Ratchev, Svetan

Abstract

This paper presents an innovative approach to integrating data-driven strategies into intelligent manufacturing systems, specifically targeting the challenges of configuration management in modular production environments. To address the distinct and evolving requirements of customized products, we propose a dynamic configuration management methodology that automatically adjusts system settings in real-time. This approach utilizes operational semantics to formalize the interactions between production modules, capturing essential operational information for intelligent decision-making. A novel control mechanism is developed, using knowledge graphs to semantically represent and manage the relationships between production system components and settings. By mapping these, the system can determine optimal configurations based on real-time data and specific operational requirements. The interaction between the control mechanism and the knowledge graph ensures continuous adaptability, enabling the system to reconfigure dynamically in response to changes. This method was validated in an industrial dry-air leak testing scenario, demonstrating its effectiveness in adaptability.

Citation

Rehman, H. U., Mo, F., Chaplin, J. C., Zarzycki, L., Jones, M., Maffei, A., & Ratchev, S. (2025). Intelligent configuration management in modular production systems: Integrating operational semantics with knowledge graphs. Journal of Manufacturing Systems, 80, 610-625. https://doi.org/10.1016/j.jmsy.2025.03.017

• Journal Article Type: Article
• Acceptance Date: Mar 11, 2025
• Online Publication Date: Apr 9, 2025
• Publication Date: 2025-06
• Deposit Date: Apr 10, 2025
• Publicly Available Date: Apr 10, 2026
• Journal: Journal of Manufacturing Systems
• Print ISSN: 0278-6125
• Electronic ISSN: 0278-6125
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 80
• Pages: 610-625
• DOI: https://doi.org/10.1016/j.jmsy.2025.03.017
• Public URL: https://nottingham-repository.worktribe.com/output/47555314