A maturity model for the autonomy of manufacturing systems

Mo, Fan; Monetti, Fabio Marco; Torayev, Agajan; Rehman, Hamood Ur; Mulet Alberola, Jose A.; Rea Minango, Nathaly; Nguyen, Hien Ngoc; Maffei, Antonio; Chaplin, Jack C.

Abstract

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model.

Citation

Mo, F., Monetti, F. M., Torayev, A., Rehman, H. U., Mulet Alberola, J. A., Rea Minango, N., …Chaplin, J. C. (2023). A maturity model for the autonomy of manufacturing systems. International Journal of Advanced Manufacturing Technology, 126(1-2), 405-428. https://doi.org/10.1007/s00170-023-10910-7

• Journal Article Type: Article
• Acceptance Date: Jan 14, 2023
• Online Publication Date: Feb 27, 2023
• Publication Date: 2023-05
• Deposit Date: Apr 25, 2023
• Publicly Available Date: Apr 26, 2023
• Journal: International Journal of Advanced Manufacturing Technology
• Print ISSN: 0268-3768
• Electronic ISSN: 1433-3015
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 126
• Issue: 1-2
• Pages: 405-428
• DOI: https://doi.org/10.1007/s00170-023-10910-7
• Keywords: Decision-making, Self-learning, Manufacturing, Digital twin, Industry 4.0, Machine learning
• Public URL: https://nottingham-repository.worktribe.com/output/19778903
• Publisher URL: https://link.springer.com/article/10.1007/s00170-023-10910-7
• Additional Information: Received: 1 October 2022; Accepted: 14 January 2023; First Online: 27 February 2023; : ; : Yes; : Yes; : Yes; : The authors declare no competing interests.