An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems

Scrimieri, Daniele; Adalat, Omar; Afazov, Shukri; Ratchev, Svetan

Abstract

Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been evaluated on a distributed robotic manufacturing system, demonstrating their efficacy. Nevertheless, the approach is general and it can be applied to other scenarios.

Citation

Scrimieri, D., Adalat, O., Afazov, S., & Ratchev, S. (2022). An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems. International Journal of Advanced Manufacturing Technology, 124(3-4), 1155-1168. https://doi.org/10.1007/s00170-022-10553-0

• Journal Article Type: Article
• Acceptance Date: Nov 15, 2022
• Online Publication Date: Nov 29, 2022
• Publication Date: Dec 29, 2022
• Deposit Date: Feb 2, 2023
• Publicly Available Date: Feb 2, 2023
• Journal: The International Journal of Advanced Manufacturing Technology
• Print ISSN: 0268-3768
• Electronic ISSN: 1433-3015
• Publisher: Springer Science and Business Media LLC
• Peer Reviewed: Peer Reviewed
• Volume: 124
• Issue: 3-4
• Pages: 1155-1168
• DOI: https://doi.org/10.1007/s00170-022-10553-0
• Keywords: Original Article, Reconfiguration, Capabilities, Multi-agent systems, Machine learning, Assembly
• Public URL: https://nottingham-repository.worktribe.com/output/15923883
• Publisher URL: https://link.springer.com/article/10.1007/s00170-022-10553-0
• Additional Information: Received: 21 September 2022; Accepted: 15 November 2022; First Online: 29 November 2022; The authors declare no competing interests.