A modular artificial intelligence and asset administration shell approach to streamline testing processes in manufacturing services

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

Abstract

The increasing demand for personalized products and cost-effectiveness has highlighted the necessity of integrating intelligence into production systems. This integration is crucial for enabling intelligent control that can adapt to variations in features, parts, and conditions, thereby enhancing functionalities while reducing costs. This research emphasizes the incorporation of intelligence in testing processes within production systems. We introduce a novel approach for controlling testing functionality using an asset administration shell enriched with modular artificial intelligence. The proposed architecture is not only effective in controlling the execution behavior through services but also offers the distinct advantage of a modular design. This modularity significantly contributes to the system's adaptability and scalability, allowing for more efficient and cost-effective solutions as different machine-learning models may be substituted to meet requirements. The effectiveness of this approach is validated through a practical use case of leak testing, demonstrating the benefits of the modular architecture in a real-world application.

Citation

Rehman, H. U., Mo, F., Chaplin, J. C., Zarzycki, L., Jones, M., & Ratchev, S. (2024). A modular artificial intelligence and asset administration shell approach to streamline testing processes in manufacturing services. Journal of Manufacturing Systems, 72, 424-436. https://doi.org/10.1016/j.jmsy.2023.12.004

• Journal Article Type: Article
• Acceptance Date: Dec 13, 2023
• Online Publication Date: Dec 23, 2023
• Publication Date: Feb 1, 2024
• Deposit Date: Jan 2, 2024
• Publicly Available Date: Jan 3, 2024
• Journal: Journal of Manufacturing Systems
• Print ISSN: 0278-6125
• Electronic ISSN: 0278-6125
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 72
• Pages: 424-436
• DOI: https://doi.org/10.1016/j.jmsy.2023.12.004
• Keywords: Industrial and Manufacturing Engineering, Hardware and Architecture, Software, Control and Systems Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/29001444
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0278612523002522?via%3Dihub