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dc.contributor.authorNannapaneni, Saideep
dc.contributor.authorMahadevan, Sankaran
dc.contributor.authorDubey, Abhishek
dc.contributor.authorLee, Yung Tsun Tina
dc.identifier.citationNannapaneni, S., Mahadevan, S., Dubey, A. et al. Online monitoring and control of a cyber-physical manufacturing process under uncertainty. J Intell Manuf (2020)en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractRecent technological advancements in computing, sensing and communication have led to the development of cyber-physical manufacturing processes, where a computing subsystem monitors the manufacturing process performance in real-time by analyzing sensor data and implements the necessary control to improve the product quality. This paper develops a predictive control framework where control actions are implemented after predicting the state of the manufacturing process or product quality at a future time using process models. In a cyber-physical manufacturing process, the product quality predictions may be affected by uncertainty sources from the computing subsystem (resource and communication uncertainty), manufacturing process (input uncertainty, process variability and modeling errors), and sensors (measurement uncertainty). In addition, due to the continuous interactions between the computing subsystem and the manufacturing process, these uncertainty sources may aggregate and compound over time. In some cases, some process parameters needed for model predictions may not be precisely known and may need to be derived from real time sensor data. This paper develops a dynamic Bayesian network approach, which enables the aggregation of multiple uncertainty sources, parameter estimation and robust prediction for online control. As the number of process parameters increase, their estimation using sensor data in real-time can be computationally expensive. To facilitate real-time analysis, variance-based global sensitivity analysis is used for dimension reduction. The proposed methodology of online monitoring and control under uncertainty, and dimension reduction, are illustrated for a cyber-physical turning process.en_US
dc.description.sponsorshipNational Institute of Standards and Technology (NIST) under the Smart Manufacturing Data Analytics Project (Cooperative Agreement No. 70NANB16H297).en_US
dc.relation.ispartofseriesJournal of Intelligent Manufacturing;2020
dc.subjectBayesian networken_US
dc.titleOnline monitoring and control of a cyber-physical manufacturing process under uncertaintyen_US
dc.rights.holder© 2020, Springer Science+Business Mediaen_US

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  • ISME Research Publications
    Research works published by faculty and students of the Department of Industrial, Systems, and Manufacturing Engineering

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