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dc.contributor.authorNannapaneni, Saideep
dc.contributor.authorMahadevan, Sankaran
dc.contributor.authorDubey, Abhishek
dc.date.accessioned2018-12-13T15:42:46Z
dc.date.available2018-12-13T15:42:46Z
dc.date.issued2018-06
dc.identifier.citationNannapaneni S, Mahadevan S, Dubey A. Real-Time Control of Cyber-Physical Manufacturing Process Under Uncertainty. ASME. International Manufacturing Science and Engineering Conference, Volume 3: Manufacturing Equipment and Systems ():V003T02A001. doi:10.1115/MSEC2018-6460en_US
dc.identifier.isbn978-0-7918-5137-1
dc.identifier.otherWOS:000451241200001
dc.identifier.urihttps://doi.org/10.1115/MSEC2018-6460
dc.identifier.urihttp://hdl.handle.net/10057/15705
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractModern manufacturing processes are increasing becoming cyber-physical in nature, where a computational system monitors the system performance, provides real-time process control by analyzing sensor data collected regarding process and product characteristics, in order to increase the quality of the manufactured product. Such real-time process monitoring and control techniques are useful in precision and ultra-precision machining processes. However, the output product quality is affected by several uncertainty sources in various stages of the manufacturing process such as the sensor uncertainty, computational system uncertainty, control input uncertainty, and the variability in the manufacturing process. The computational system may be a single computing node or a distributed computing network; the latter scenario introduces additional uncertainty due to the communication between several computing nodes. Due to the continuous monitoring process, these uncertainty sources aggregate and compound over time, resulting in variations of product quality. Therefore, characterization of the various uncertainty sources and their impact on the product quality are necessary to increase the efficiency and productivity of the overall manufacturing process. To this end, this paper develops a two-level dynamic Bayesian network methodology, where the higher level captures the uncertainty in the sensors, control inputs, and the manufacturing process while the lower level captures the uncertainty in the communication between several computing nodes. In addition, we illustrate the use of a variance-based global sensitivity analysis approach for dimension reduction in a high-dimensional manufacturing process, in order to enable real-time analysis for process control. The proposed methodologies of process 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) [70NANB16H297].en_US
dc.language.isoen_USen_US
dc.publisherASMEen_US
dc.relation.ispartofseriesASME 2018 13th International Manufacturing Science and Engineering Conference;v.3
dc.subjectManufacturingen_US
dc.subjectReal-time controlen_US
dc.subjectUncertaintyen_US
dc.titleReal-time control of cyber-physical manufacturing process under uncertaintyen_US
dc.typeConference paperen_US
dc.rights.holder© 2018 by ASMEen_US


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