A Study on Performance Evaluation and Status-based Decision for Cyber-Physical Production Systems
Yan Lu, Feifan Wang, Feng Ju
In concert with the advances in information and communication technology and its applications to manufacturing environments, physical entities in factories are acquiring more intelligence via the integration with cyber systems. This integration brings about Cyber-Physical Production Systems and leads to smart manufacturing, the next generation manufacturing paradigm. In the new paradigm, high level of agility, flexibility and real-time control becomes possible to keep the system running efficiently and self-organized. Meanwhile, it becomes difficult in these self- organized and decentralized systems to capture the systems status, evaluate the systems performance, and predict the systems future events. In this article, we search for a solution for smart manufacturing systems where the intelligence from smart entities could be fully utilized, while the control of the system would not be lost. To achieve this goal, a solution to integrating schedule-driven production (push systems) and event-driven production (pull systems) is provided to optimize both material flow and information flow for manufacturing operations. For each entity in a smart manufacturing system, details of decision making are encapsulated and its status is exposed. The status-based decisions filter out unimportant information and make smart manufacturing systems loosely-coupled and predictable. A simulation case study based on Devices Profile for Web Services is used to illustrate the effectiveness of such an approach. The case study suggests that status-based decisions could be applied to smart manufacturing, and they can be part of an approach that balances the self-organized control and overall performance evaluation.
13th IEEE Conference on Automation Science and Engineering
, Wang, F.
and Ju, F.
A Study on Performance Evaluation and Status-based Decision for Cyber-Physical Production Systems, 13th IEEE Conference on Automation Science and Engineering, Xi'An, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923404
(Accessed December 10, 2023)